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The Energy Institute Statistical Review of World Energy™ analyses data on world energy markets from the prior year. Previously produced by bp, the Review has been providing timely, comprehensive and objective data to the energy community since 1952.

World Electricity access database Hundreds of millions of people have gained access to modern energy over the last two decades, especially in China and India. Rapid economic development in several developing countries, increasing urbanisation and ongoing energy access programmes have been important factors in this achievement. The IEA Access to Energy database provides a snapshot of progress made toward meeting the ultimate goal of universal access. Note: For indicator population without access, value 1 represent <1 except Botswana, Guatemala countries

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Data source(s) used: Migration and calculation of foreign resident population and structure by citizenship : Istat since 2011 has been producing estimates on characteristics (citizenship of origin, gender and age) of the new Italian citizens and on the different ways of access to citizenship (marriage, residence, etc.). The data are also provided to Eurostat for Regulation (EC) 862/2007. The estimates are produced using different data sources. Istat: municipal registry lists Ministry of Interior: datasets on acquisitions of citizenship.

This dataset contains data for the African countries and aggregate countries such as crude oil producers ; Sub-Saharan Africa. Data is organized under sixteen socio-economic chapters such as prices, energy, agriculture, social and AFDB aggregates which covers country scores in regional integration, social protection among others.

Data cited at: The African Development Bank: The Africa Infrastructure Development Index (AIDI): https://infrastructureafrica.opendataforafrica.org/pbuerhd https://dataportal.opendataforafrica.org/AIDI The Africa Infrastructure Development Index (AIDI) is produced by the African Development Bank. The AIDI serves a number of key objectives, principally: (i) to monitor and evaluate the status and progress of infrastructure development across the continent; (ii) to assist in resource allocation within the framework of ADF replenishments; and (iii) to contribute to policy dialogue within the Bank and between the Bank, RMCs and other development organizations.

Data cited at: https://dataportal.opendataforafrica.org/zucdnfg

Data cited at: https://dataportal.opendataforafrica.org/wzambje

Data cited at: https://dataportal.opendataforafrica.org/wwoyzgb

Data cited at: https://dataportal.opendataforafrica.org/frcclfe

Agency Revenue By Government Donor for assessed revenue type

This dataset provides data for foreign tourist arrivals distributed by age  group.

Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILO estimates and projections methodological note.

The data describe the average use of chemical and mineral fertilizers per area of cropland (arable land and permanent crops) at national, regional, and global level in a time series from 2002 to 2014The data describe the average use of chemical and mineral fertilizers per area of cropland (arable land and permanent crops) at national, regional, and global level in a time series from 2002 to 2015

The Agri-environmental Indicators—Land domain provides information on the annual evolution of the distribution of agricultural and forest areas, and their sub-components, including irrigated areas, at national, regional and global levels.

The Livestock Patterns domain of the FAOSTAT Agri-Environmental Indicators contains data on livestock numbers, shares of major livestock species and livestock densities in the agricultural area. Values are calculated using Livestock Units (LSU), which facilitate aggregating information for different livestock types. Data are available by country, with global coverage, for the period 1961–2014. This methodology applies the LSU coefficients reported in the "Guidelines for the preparation of livestock sector reviews" (FAO, 2011). From this publication, LSU coefficients are computed by livestock type and by country. The reference unit used for the calculation of livestock units (=1 LSU) is the grazing equivalent of one adult dairy cow producing 3000 kg of milk annually, fed without additional concentrated foodstuffs. FAOSTAT agri-environmental indicators on livestock patterns closely follow the structure of the indicators in EUROSTAT.

The data describe the average use of pesticides per area of cropland (arable land and permanent crops) at national level in a time series from 1990 to 2014. 

Refers to Changi Airport only. Data exclude passenger baggage, diplomatic cargo and aircraft stores. Data breakdown is only available for the 11 countries listed above, and they do not sum up to the total of the respective regions.

Refers to Changi Airport only. Data exclude passenger baggage, diplomatic cargo and aircraft stores. Data breakdown is only available for the 11 countries listed above, and they do not sum up to the total of the respective regions.

Data exclude transit passengers who continued their journey on the same flight. Figures from January 1989 refer to Changi Airport only. Data breakdown is only available for the 11 countries listed above, and they do not sum up to the total of the respective regions.

Data exclude transit passengers who continued their journey on the same flight. Figures from January 1989 refer to Changi Airport only. Data breakdown is only available for the 11 countries listed above, and they do not sum up to the total of the respective regions.

Data exclude transit passengers who continued their journey on the same flight. Figures from January 1989 refer to Changi Airport only. Data breakdown is only available for the 11 countries listed above, and they do not sum up to the total of the respective regions.

Data exclude transit passengers who continued their journey on the same flight. Figures from January 1989 refer to Changi Airport only. Data breakdown is only available for the 11 countries listed above, and they do not sum up to the total of the respective regions.

General aviation operations are defined by the FAA based Source: FAA Operations Network (OPSNET) on the traffic operations counted in the OPSNET. Air Traffic Control data shows federal, non-federal, and military through 2005, while 2006 through 2011 are FAA and contract.    TRANSLATE with xEnglishArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian  TRANSLATE with COPY THE URL BELOW BackEMBED THE SNIPPET BELOW IN YOUR SITEEnable collaborative features and customize widget: Bing Webmaster PortalBack

Data cited at: The World Bank https://datacatalog.worldbank.org/ Topic: All The Ginis Dataset Publication: https://datacatalog.worldbank.org/dataset/all-ginis-dataset License: http://creativecommons.org/licenses/by/4.0/   This dataset includes combined and standardized Gini data from eight original sources: Luxembourg Income Study (LIS), Socio-Economic Database for Latin America (SEDLAC), Survey of Living Conditions (SILC) by Eurostat, World Income Distribution (WYD; the full data set is available here), World Bank Europe and Central Asia dataset, World Institute for Development Research (WIDER), World Bank Povcal, and Ginis from individual long-term inequality studies (just introduced in this version).

Data source(s) used: Crimes reported to the Judicial authorities by the State Police, Carabinieri and Guardia di Finanza: Are processed the data on felonies and people who were reported by police to the court Other data characteristics: Data referring to social demographic characteristics of alleged offenders could not coincide with data on reports because of the different timing of extraction from police forces database.The sum of the crimes by province could not coincide with the total of the region, and that of the regions with the total Italy, because of the missed precise statement, for some crimes, of the place where they have been committed (or of the region of the committed crime but not of the province).

Data cited at: https://dataportal.opendataforafrica.org/zapahme

Structural business statistics (SBS) describes the structure, conduct and performance of economic activities, down to the most detailed activity level (several hundred economic sectors). SBS are transmitted annually by the EU Member States on the basis of a legal obligation from 1995 onwards.   SBS covers all activities of the business economy with the exception of agricultural activities and personal services and the data are provided by all EU Member States, Norway and Switzerland, some candidate and potential candidate countries. The data are collected by domain of activity (annex) : Annex I - Services, Annex II - Industry, Annex III - Trade and Annex IV- Constructions and by datasets. Each annex contains several datasets as indicated in the SBS Regulation. The majority of the data is collected by National Statistical Institutes (NSIs) by means of statistical surveys, business registers or from various administrative sources. Regulatory or controlling national offices for financial institutions or central banks often provide the information required for the financial sector (NACE Rev 2 Section K / NACE Rev 1.1 Section J). Member States apply various statistical methods, according to the data source, such as grossing up, model based estimation or different forms of imputation, to ensure the quality of SBSs produced. Main characteristics (variables) of the SBS data category: Business Demographic variables (e.g. Number of enterprises)"Output related" variables (e.g. Turnover, Value added)"Input related" variables: labour input (e.g. Employment, Hours worked); goods and services input (e.g. Total of purchases); capital input (e.g. Material investments) All SBS characteristics are published on Eurostat’s website by tables and an example of the existent tables is presented below: Annual enterprise statistics: Characteristics collected are published by country and detailed on NACE Rev 2 and NACE Rev 1.1 class level (4-digits). Some classes or groups in 'services' section have been aggregated.Annual enterprise statistics broken down by size classes: Characteristics are published by country and detailed down to NACE Rev 2 and NACE Rev 1.1 group level (3-digits) and employment size class. For trade (NACE Rev 2 and NACE Rev 1.1 Section G) a supplementary breakdown by turnover size class is available.Annual regional statistics: Four characteristics are published by NUTS-2 country region and detailed on NACE Rev 2 and NACE Rev 1.1 division level (2-digits) (but to group level (3-digits) for the trade section). More information on the contents of different tables: the detail level and breakdowns required starting with the reference year 2008 is defined in Commission Regulation N° 251/2009. For previous reference years it is included in Commission Regulations (EC) N° 2701/98 and amended by Commission Regulation N°1614/2002 and Commission Regulation N°1669/2003. Several important derived indicators are generated in the form of ratios of certain monetary characteristics or per head values. A list with the available derived indicators is available below in the Annex.

The Financial Balance Sheets show financial assets, liabilities and net financial worth (assets minus liabilities) at the end of the period. This table is on a consolidated basis, which means that counterpart assets and liabilities of units within the same sector or subsector (or the economy as a whole) have been removed. In this table, the presentation is on a country-by-country basis. Users are recommended to select one country (or area) at a time in the ‘Reference area’ filter. The default view of the table is for the economy as a whole, but you can use the ‘Institutional sector’ filter to select specific sectors such as Non-financial Corporations, Financial Corporations, General Government and Households, as well as the Rest of the world account. For each sector, the table presents breakdowns by financial instrument, for example currency and deposits, debt securities, loans, equity and investment fund shares, insurance and pensions. Users can also choose to compare a single financial instrument, such as listed shares, for several countries. Users should note that some countries do not produce consolidated accounts for all sectors. These indicators were presented in the previous dissemination system in the SNA_TABLE710R dataset. Explore also the OECD Financial Accounts and Balance Sheets webpage: Financial Accounts and Balance Sheets webpage OECD statistics contact: [email protected]

Statistics Name: Current Survey of Supply and Demand for Petroleum Products

Data Dictionary: https://github.com/wri/Aqueduct40/blob/master/data_dictionary_country-rankings.md#country-future  

Title: Area / Population And Income Of G-20 Countries   Description: The data refers to for G-20 countries details on area, population, gross domestic product (GDP), domestic product per capita income and purchasing power from 2010 to 2015.    

Eurostat Dataset Id:tour_occ_arnrmw National data Monthly and annual data on arrivals, nights spent and occupancy rates at tourist accommodation establishments. Regional data Annual arrivals, nights spent at tourist accommodation establishments at NUTS 2 level. Please note that for paragraphs where no metadata for regional data has been specified, the regional metadata is identical to the metadata provided for the national data.

APD Regional Economic Outlook (REO) provides information on recent economic developments and prospects for countries in Asia and Pacific. Data for the REO for Asia and Pacific is prepared in conjunction with the semi-annual World Economic Outlook (WEO) exercises, spring and fall. Data are consistent with the projections underlying the WEO. REO aggregate data may differ from WEO aggregates due to differences in group membership. Composite data for country groups are weighted averages of data for individual countries. Arithmetic weighted averages are used for all concepts except for inflation and broad money, for which geometric averages are used. PPP GDP weights from the WEO database are used for the aggregation of real GDP growth, real non-oil GDP growth, real per capita GDP growth, investment, national savings, broad money, claims on the nonfinancial private sector, and real and nominal effective exchange rates. Aggregates for other concepts are weighted by GDP in U.S. dollars at market exchange rates.

1.Data cited at AutoAlliance.org - http://www.autoalliance.org/files/dmfile/2015-Auto-Industry-Jobs-Report.pdf 2. Light-Duty (0-6,000 lb)-Class 1     Light-Duty (6,001-10,000 lb)-Class 2     Medium-Duty (10,001-14,000 lb)-Class 3     Medium-Duty (14,001-16,000 lb)-Class 4     Medium-Duty (16,001-19,500 lb)-Class 5     Medium-Duty (19,501-26,000 lb)-Class 6     Heavy-Duty (26,001-33,000 lb)-Class 7     Heavy-Duty (33,001 lb and over)-Class 8

This table contains data on average usual weekly hours worked in the main job broken down by total employment, full-time employment and part-time employment. Actual hours of work instead of usual hours of work are only available in some countries (Mexico and Korea). Data are further broken down by professional status - employees, total employment - by sex and standardised age groups (15-24, 25-54, 55-64, 65+ and total).

Sugar Data of United States

UNCTAD's Bilateral FDI Statistics provides up-to-date and systematic FDI data for 206 economies around the world, covering inflows (table 1), outflows (table 2), inward stock (table 3) and outward stock (table 4) by region and economy. Data are in principle collected from national sources. In order to cover the entire world, where data are not available from national sources, data from partner countries (mirror data) as well as from other international organizations have also been used.

The residential property price statistics collect data from different countries. The BIS has obtained permission from various national data providers, with the assistance of its member central banks, to disseminate these statistics. The topic ‘Property prices: Selected series,’ contains nominal and real quarterly values for 58 countries, both in levels and in growth rates (ie four series per country). Real series are the nominal price series deflated by the consumer price index. The BIS has made the selection based on the Handbook on Residential Property Prices and the experience and metadata of central banks.   Data cited at : https://www.bis.org/statistics/index.htm

Data cited at: Havocscope

Note: No further updates planned by source Data cited at: The World Bank https://datacatalog.worldbank.org/ Topic: Jobs Publication: https://datacatalog.worldbank.org/dataset/jobs License: http://creativecommons.org/licenses/by/4.0/   The World Bank Jobs Statistics Over 150 indicators on labor-related topics, covering over 200 economies from 1990 to present.

Data cited: Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2016 (GBD 2016) Cancer Incidence, Mortality, Years of Life Lost, Years Lived with Disability, and Disability-Adjusted Life Years 1990-2016. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2018.   The Global Burden of Disease Study 2016 (GBD 2016), coordinated by the Institute for Health Metrics and Evaluation (IHME), estimated the burden of diseases, injuries, and risk factors for 195 countries and territories and at the subnational level for a subset of countries. Estimates for deaths, disability-adjusted life years (DALYs), years lived with disability (YLDs), years of life lost (YLLs), prevalence, and incidence for 29 cancer groups by age and sex for 1990-2016 are available from the GBD Results Tool. Files available in this record are the web tables published in JAMA Oncology in June 2018 in "Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-years for 29 Cancer Groups, 1990 to 2016."

OECD Indicators on Carbon dioxide (CO2) emissions embodied in international trade (TeCO2) are derived by combining the 2021 editions of OECD Inter-Country Input-Output (ICIO) Database and of International Energy Agency (IEA) statistics on CO2 emissions from fuel combustion. In this release of TeCO2, emissions from fuels used for international aviation and maritime transport (i.e. aviation and marine bunkers) are also considered.Production-based CO2 emissions are estimated by allocating the CO2 emissions to the 45 target industries in OECD ICIO and, to household final consumption of fuels, by both residents and non-residents.Demand-based CO2 emissions are calculated by multiplying the intensities of the production-based emissions (c) with the global Leontief inverse (I-A)(-1) and global final demand matrix (Y) from OECD ICIO, taking the column sums of the resulting matrix and adding residential and private road emissions (FNLC), i.e. direct emissions from final demand: colsum [ diag(c) (I-A)(-1) Y ] + FNLC.For more information, see TeCO2 web page: http://oe.cd/io-co2.

Data on causes of death (COD) provide information on mortality patterns and form a major element of public health information. COD data refer to the underlying cause which - according to the World Health Organisation (WHO) - is "the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury". Causes of death are classified by the 86 causes of the "European shortlist" of causes of death. This shortlist is based on the International Statistical Classification of Diseases and Related Health Problems (ICD). COD data are derived from death certificates. The medical certification of death is an obligation in all Member States. Countries code the information provided in the medical certificate of cause of death into ICD codes according to the rules specified in the ICD. Data are broken down by sex, 5-year age groups, cause of death and by residency and country of occurrence. For stillbirths and neonatal deaths additional breakdows might include age of mother. Data are available for EU-28, the former Yugoslav Republic of Macedonia, Albania, Iceland, Norway, Liechtenstein and Switzerland. Regional data (NUTS level 2) are available for most of the countries. Annual national data are provided in absolute number, crude death rates and standardised death rates. At regional level (NUTS level 2) the same is provided in form of 3 years averages. Annual crude death rates are also available at NUTS level 2.

Data on causes of death (COD) provide information on mortality patterns and form a major element of public health information. COD data refer to the underlying cause which - according to the World Health Organisation (WHO) - is "the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury". Causes of death are classified by the 86 causes of the "European shortlist" of causes of death. This shortlist is based on the International Statistical Classification of Diseases and Related Health Problems (ICD). COD data are derived from death certificates. The medical certification of death is an obligation in all Member States. Countries code the information provided in the medical certificate of cause of death into ICD codes according to the rules specified in the ICD. Data are broken down by sex, 5-year age groups, cause of death and by residency and country of occurrence. For stillbirths and neonatal deaths additional breakdows might include age of mother. Data are available for EU-28, the former Yugoslav Republic of Macedonia, Albania, Iceland, Norway, Liechtenstein and Switzerland. Regional data (NUTS level 2) are available for most of the countries. Annual national data are provided in absolute number, crude death rates and standardised death rates. At regional level (NUTS level 2) the same is provided in form of 3 years averages. Annual crude death rates are also available at NUTS level 2.

Data on causes of death (COD) provide information on mortality patterns and form a major element of public health information. COD data refer to the underlying cause which - according to the World Health Organisation (WHO) - is "the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury". Causes of death are classified by the 86 causes of the "European shortlist" of causes of death. This shortlist is based on the International Statistical Classification of Diseases and Related Health Problems (ICD). COD data are derived from death certificates. The medical certification of death is an obligation in all Member States. Countries code the information provided in the medical certificate of cause of death into ICD codes according to the rules specified in the ICD. Data are broken down by sex, 5-year age groups, cause of death and by residency and country of occurrence. For stillbirths and neonatal deaths additional breakdows might include age of mother. Data are available for EU-28, the former Yugoslav Republic of Macedonia, Albania, Iceland, Norway, Liechtenstein and Switzerland. Regional data (NUTS level 2) are available for most of the countries. Annual national data are provided in absolute number, crude death rates and standardised death rates. At regional level (NUTS level 2) the same is provided in form of 3 years averages. Annual crude death rates are also available at NUTS level 2.

Data on causes of death (COD) provide information on mortality patterns and form a major element of public health information. COD data refer to the underlying cause which - according to the World Health Organisation (WHO) - is "the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury". Causes of death are classified by the 86 causes of the "European shortlist" of causes of death. This shortlist is based on the International Statistical Classification of Diseases and Related Health Problems (ICD). COD data are derived from death certificates. The medical certification of death is an obligation in all Member States. Countries code the information provided in the medical certificate of cause of death into ICD codes according to the rules specified in the ICD. Data are broken down by sex, 5-year age groups, cause of death and by residency and country of occurrence. For stillbirths and neonatal deaths additional breakdows might include age of mother. Data are available for EU-28, Iceland, Norway, Liechtenstein, Switzerland, Serbia and Turkey. Regional data (NUTS level 2) are available for most of the countries. Annual national data are provided in absolute number, crude death rates and standardised death rates. At regional level (NUTS level 2) the same is provided in form of 3 years averages. Annual crude death rates are also available at NUTS level 2.

Report: International Trade, Germany, 2018 Source Table Details: Page : From 119 to 172 Table : From 60a to 70

Global and regional deaths of children under 5 years of age by cause. Estimates generated by the WHO and Maternal and Child Epidemiology Estimation Group (MCEE) 2018.

This indicator is based on harmonized definitions used by the ILO in the context of producing global estimates. Data may differ from nationally reported figures. Children refer to persons ages 5-17 years old. For more information, refer to the concepts and definitions page.

This indicator is based on harmonized definitions used by the ILO in the context of producing global estimates. Data may differ from nationally reported figures. Children refer to persons ages 5-17 years old. For more information, refer to the concepts and definitions page.

This indicator is based on harmonized definitions used by the ILO in the context of producing global estimates. Data may differ from nationally reported figures. Child labour includes: (a) children 5-11 years old who did at least one hour of economic activity and/or more than 21 hours of unpaid household services, (b) children 12-14 years old who did at least 14 hours of economic activity and/or more than 21 hours of unpaid household services, and (c) children 15-17 years old who did at least 43 hours of economic activity. For more information, refer to the concepts and definitions page.

This indicator is based on harmonized definitions used by the ILO in the context of producing global estimates. Data may differ from nationally reported figures. Child labour includes: (a) children 5-11 years old who did at least one hour of economic activity and/or more than 21 hours of unpaid household services, (b) children 12-14 years old who did at least 14 hours of economic activity and/or more than 21 hours of unpaid household services, and (c) children 15-17 years old who did at least 43 hours of economic activity. For more information, refer to the concepts and definitions page.

This indicator is based on harmonized definitions used by the ILO in the context of producing global estimates. Data may differ from nationally reported figures. Child labour includes: (a) children 5-11 years old who did at least one hour of economic activity and/or more than 21 hours of unpaid household services, (b) children 12-14 years old who did at least 14 hours of economic activity and/or more than 21 hours of unpaid household services, and (c) children 15-17 years old who did at least 43 hours of economic activity. For more information, refer to the concepts and definitions page.

This indicator is based on harmonized definitions used by the ILO in the context of producing global estimates. Data may differ from nationally reported figures. Child labour includes: (a) children 5-11 years old who did at least one hour of economic activity and/or more than 21 hours of unpaid household services, (b) children 12-14 years old who did at least 14 hours of economic activity and/or more than 21 hours of unpaid household services, and (c) children 15-17 years old who did at least 43 hours of economic activity. For more information, refer to the concepts and definitions page.

This indicator is based on harmonized definitions used by the ILO in the context of producing global estimates. Data may differ from nationally reported figures. Child labour includes: (a) children 5-11 years old who did at least one hour of economic activity and/or more than 21 hours of unpaid household services, (b) children 12-14 years old who did at least 14 hours of economic activity and/or more than 21 hours of unpaid household services, and (c) children 15-17 years old who did at least 43 hours of economic activity. For more information, refer to the concepts and definitions page.

This indicator is based on harmonized definitions used by the ILO in the context of producing global estimates. Data may differ from nationally reported figures. This indicator refers to children ages 5-17 years old engaged in any form of market production and certain types of non-market production (principally that of goods such as agricultural produce for own use). This includes work in both the formal and informal economy, inside and outside family settings, for pay or profit (cash or in-kind, part-time or full-time) and domestic work outside the child's own household for an employer (paid or unpaid). For more information, refer to the concepts and definitions page.

This indicator is based on harmonized definitions used by the ILO in the context of producing global estimates. Data may differ from nationally reported figures. This indicator refers to children ages 5-17 years old engaged in any form of market production and certain types of non-market production (principally that of goods such as agricultural produce for own use). This includes work in both the formal and informal economy, inside and outside family settings, for pay or profit (cash or in-kind, part-time or full-time) and domestic work outside the child's own household for an employer (paid or unpaid). For more information, refer to the concepts and definitions page.

This indicator is based on harmonized definitions used by the ILO in the context of producing global estimates. Data may differ from nationally reported figures. This indicator refers to children ages 5-17 years old engaged in any form of market production and certain types of non-market production (principally that of goods such as agricultural produce for own use). This includes work in both the formal and informal economy, inside and outside family settings, for pay or profit (cash or in-kind, part-time or full-time) and domestic work outside the child's own household for an employer (paid or unpaid). For more information, refer to the concepts and definitions page.

This indicator is based on harmonized definitions used by the ILO in the context of producing global estimates. Data may differ from nationally reported figures. This indicator refers to children ages 5-17 years old engaged in any form of market production and certain types of non-market production (principally that of goods such as agricultural produce for own use). This includes work in both the formal and informal economy, inside and outside family settings, for pay or profit (cash or in-kind, part-time or full-time) and domestic work outside the child's own household for an employer (paid or unpaid). For more information, refer to the concepts and definitions page.

This indicator is based on harmonized definitions used by the ILO in the context of producing global estimates. Data may differ from nationally reported figures. This indicator refers to children ages 5-17 years old engaged in any form of market production and certain types of non-market production (principally that of goods such as agricultural produce for own use). This includes work in both the formal and informal economy, inside and outside family settings, for pay or profit (cash or in-kind, part-time or full-time) and domestic work outside the child's own household for an employer (paid or unpaid). For more information, refer to the concepts and definitions page.

Preliminary Release: China's Total Export & Import Values ​​by Country/Region (in USD)

Starting from 1750 Population statistics have been digitised into PDF format in the National Library's Doria service: Publications on Population structure and Vital statistics in Doria (in Finnish) Publications on Population censuses in Doria (in Finnish) --- Description of statistic Quality descriptions Concepts and definitionsRegionThese statistics apply the regional division of 1 January 2019 to the whole time series.NationalityCountries (ISO 3166). The used classification of continents is the classification of Eurostat, where Cyprus and Turkey belong to Europe. In the classification of continents, Europe does not include the figures for Finland. Non-autonomous states are combined with the mother country. Czech Republic = Czech Republic + Former Czechoslovakia Sudan = Sudan + Former Sudan If a person has two nationalities and one of them is Finnish, he/she will be included in statistics as a Finnish national. --- HDI = Human Development Index Ranking according to the 2016 HDI index by country: The former Soviet Union is included in the Russia HDI category The former Czechoslovakia is included in the Czech Republic HDI category The former Yugoslavia and Serbia and Montenegro are included in the Serbia HDI categoryAgeAge refers to a person's age in whole years as at 31 December.InformationPopulation 31 DecPopulation at the end of the statistical reference period.

... confidentialFor reasons of privacy protection, cells with less than 10 cases of citizenship by municipality have been marked with three dots. Starting from 1750 Population statistics have been digitized into PDF format in the National Library's Doria service: Publications on Population structure and Vital statistics in Doria (in Finnish) Publications on Population censuses in Doria (in Finnish) --- Description of statistic Quality descriptions Concepts and definitionsAreaThese statistics apply the regional division of 1 January 2019 to the whole time series. Data for merged municipalities have been combined. Partial municipal mergers have not been taken into account in the years preceding the merger.NationalityCountries (ISO 3166). The used classification of continents is the classification of Eurostat, where Cyprus and Turkey belong to Europe. In the classification of continents, Europe does not include the figures for Finland. Non-autonomous states are combined with the mother country. Czech Republic = Czech Republic + Former Czechoslovakia Sudan = Sudan + Former Sudan If a person has two nationalities and one of them is Finnish, he/she will be included in statistics as a Finnish national. --- HDI = Human Development Index Ranking according to the 2016 HDI index by country: The former Soviet Union is included in the Russia HDI category The former Czechoslovakia is included in the Czech Republic HDI category The former Yugoslavia and Serbia and Montenegro are included in the Serbia HDI categoryInformationPopulation 31 DecPopulation at the end of the statistical reference period.

Data cited at: Statistics Finland http://www.stat.fi/index_en.html Publication: 030 -- Citizenship by sex, by region and municipality in 1990 to 2017 http://pxnet2.stat.fi/PXWeb/pxweb/en/StatFin/StatFin__vrm__vaerak/statfin_vaerak_pxt_030.px License: http://creativecommons.org/licenses/by/4.0/ Concepts and definitions Description Quality descriptionThese statistics apply the regional division of 1 January 2018 to the whole time series.Population statistics from 1750 to 2000 have been digitised into PDF format in the National Library's Doria service.Publications on Population structure and vital statistics in Doria (in Finnish) Publications on Population censuses in Doria (in Finnish)AreaFor reasons of privacy protection, cells with less than 10 cases of citizenship, country of birth, background country or language by municipality have been marked with two dots. Continent sums have not been hidden in municipality data nor have regional data concerning individual languages or countries.CitizenshipIf a person has two nationalities and one of them is Finnish, he/she will be included in statistics as a Finnish national. The used classification of continents is the classification of Eurostat, where Cyprus and Turkey belong to Europe. Citizens of non-autonomous states are summed under the mother country.CitizenshipCzech RepublicCzech Republic + Former CzechoslovakiaSudanSudan + Former Sudan

The Population Register contains information on all persons who have resided in Greenland after 1 January 1977. The purpose of the register is to be the basis for population statistics, and to supplement other personal information with basic information about each person, like address and family relations.The Population register is updated with information from CPR (Administrative Population Register) where the following information is retrieved: name, gender, age, place of birth, citizenship, marital status, reference to mother, father and spouse, address of residence and more. According to §13 of the Act on Greenland Statistics, no person-related information is disclosed from the register, except for personal numbers, randomly drawn for surveys

Population as of 1 Juli. Our Populationregister holds information on all persons, who have had address in Greenland after Januar 1st 1977. Purpose of the register Registrets formål er at danne grundlag for befolkningsstatistikken, samt supplere andre personhenførbare oplysninger med basisoplysninger om personen, samt dennes adresse og familierelationer. Befolkningsstatistikregistret opdateres med oplysninger fra CPR (Folkeregistrene) hvor følgende oplysninger hentes: navn, køn, alder, fødested, statsborgerskab, civilstand, henvisning til mor, far samt ægtefælle, bopælsadresse og tilflytningsdato. Jfr §13 i Lov om Grønlands Statistik, videregives ingen personhenførbare oplysninger fra registret, bortset fra personnumre, som efter Datatilsynets godkendelse er udtrukket til interviewundersøgelser

Data cited at: The World Bank  https://datacatalog.worldbank.org/ Topic:Climate Investment Funds – Clean Technology Fund (CTF) Results Data Publication: https://finances.worldbank.org/Projects/2019-Climate-Investment-Funds-Clean-Technology-Fun/kjmm-jfbk License: http://creativecommons.org/licenses/by/4.0/ 

Meteorology and Hydrology Statistics of Lao PDR. This data set contains information of The statistics yearbook 2017 is compiled and published by Lao statistics Bureau, Ministry of Planning and Investment. Lao statistics Bureau of expressing gratitude provincial statistics Centre, line ministries involved, ministry-equivalent agencies and other parties to contribute to compile statistical data Economic, Social and Natural Resources and Environment cooperation throughout, Lao Statistics Bureau welcome the proposal, comments and feedback to guide the improvement this magazines better. Meanwhile, the Lao Statistics Bureau express thanks to highly among those interested in using information and statistics to Lao Statistics Bureau.

Food supply data is some of the most important data in FAOSTAT. In fact, this data is for the basis for estimation of global and national undernourishment assessment, when it is combined with parameters and other data sets. This data has been the foundation of food balance sheets ever since they were first constructed. The data is accessed by both business and governments for economic analysis and policy setting, as well as being used by the academic community.

The FAOSTAT monthly CPI Food CPI database was based on the ILO CPI data until December 2014. In 2014, IMF-ILO-FAO agreed to transfer global CPI data compilation from ILO to IMF. Upon agreement, CPIs for all items and its sub components originates from the International Monetary Fund (IMF), and the UN Statistics Division(UNSD) for countries not covered by the IMF. However, due to a limited time coverage from IMF and UNSD for a number of countries, the Organisation for Economic Co-operation and Development (OECD), the Latin America and the Caribbean statistics (CEPALSTAT), Central Bank of Western African States (BCEAO), Eastern Caribbean Central Bank (ECCB) and national statistical office website data are used for missing historical data from IMF and UNSD food CPI. The FAO CPI dataset for all items(or general CPI) and the Food CPI, consists of a complete and consistent set of time series from January 2000 onwards. These indices measure the price change between the current and reference periods of the average basket of goods and services purchased by households. The CPI,all items is typically used to measure and monitor inflation, set monetary policy targets, index social benefits such as pensions and unemployment benefits, and to escalate thresholds and credits in the income tax systems and wages in public and private wage contracts.

Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILO estimates and projections methodological note.

The Tax Foundation’s publication Corporate Tax Rates around the World shows how statutory corporate income tax rates have developed since 1980, with data for over 200 jurisdictions for the year 2024. The dataset we compiled for the years 1980 to 2024 is made available as a resource for research.  

Data cited at NUMBEO Numbeo is the world’s largest database of user-contributed data about cities and countries worldwide. Numbeo provides current and timely information on world living conditions including cost of living, housing indicators, health care, traffic, crime and pollution. For more information please check http://www.numbeo.com/cost-of-living/rankings_by_country.jsp   About dataset: These indices are relative to New York City (NYC). Which means that for New York City, each index should be 100(%). If another city has, for example, rent index of 120, it means rents in average in that city are 20% more expensive than in New York City. If a city has rent index of 70, that means in the average in that city rents are 30% less expensive than in New York City. Cost of Living Index (Excl. Rent) is a relative indicator of consumer goods price, including groceries, restaurants, transportation and utilities. Cost of Living Index doesn't include accommodation expenses such as rent or mortgage. If a city has a Cost of Living Index of 120, it means Numbeo estimates it is 20% more expensive than New York (excluding rent). Rent Index is estimation of prices of renting apartments in the city compared to New York City. If Rent index is 80, Numbeo estimates that price for renting in that city is 80% of price in New York. Groceries Index is an estimation of grocery prices in the city compared to New York City. To calculate this section, Numbeo uses "Markets"section of each city. Restaurants Index is a comparison of prices of meals and drinks in restaurants and bars compared to NYC. Cost of Living Plus Rent Index is an estimation of consumer goods prices including rent in the city comparing to New York City. Local Purchasing Power shows relative purchasing power in buying goods and services in a given city for the average wage in that city. If domestic purchasing power is 40, this means that the inhabitants of that city with the average salary can afford to buy 60% less typical goods and services than New York City residents with an average salary.

Chinese Taipei is WTO terminology for the territory referred to by the United Nations as Taiwan Province of China. Please also refer to the official announcement by WTO at www.wto.org/english/news_e/pres01_e/pr253_e.htm As of 2017, the data includes an additional six entities (CTBTO, ICC, UNCDF, UNFCCC, UNRISD, UNSSC)

Bloom Consulting was founded in 2003 as a Nation Branding consultancy. Our Headquarters are located in Madrid, with offices in Lisbon and São Paulo. Bloom Consulting has been interviewed by The Economist, Forbes and CNN . According to Country Branding Central www.countrybrandingwiki.org, our CEO José Filipe Torres, a recurrent lecturer in Universities such as Harvard, is considered one of the top 3 international experts in the field of Nation Branding, Region and City Branding, providing advisory for the OECD. In addition, Bloom Consulting publishes the Bloom Consulting Country Brand Ranking © annually for both Trade and Tourism, to extensively analyze the brand performance of 193 countries and territories worldwide and the Digital Country Index - Measuring the Brand appeal of countries and territories in the Digital World.

Citation: Damodaran, Aswath, Equity Risk Premiums (ERP): Determinants, Estimation and Implications – The 2016 Edition (March 5, 2016). Available at SSRN: https://ssrn.com/abstract=2742186 or http://dx.doi.org/10.2139/ssrn.2742186   This dataset summarizes the latest bond ratings and appropriate default spreads for different countries. While you can use these numbers as rough estimates of country risk premiums, you may want to modify the premia to reflect the additional risk of equity markets. To estimate the long term country equity risk premium, I start with a default spread, which I obtain in one of two ways: (1) I use the local currency sovereign rating (from Moody's: www.moodys.com) and estimate the default spread for that rating (based upon traded country bonds) over a default free government bond rate. For countries without a Moody's rating but with an S&P rating, I use the Moody's equivalent of the S&P rating. To get the default spreads by sovereign rating, I use the CDS spreads and compute the average CDS spread by rating. Using that number as a basis, I extrapolate for those ratings for which I have no CDS spreads. (2) I start with the CDS spread for the country, if one is available and subtract out the US CDS spread, since my mature market premium is derived from the US market. That difference becomes the country spread. For the few countries that have CDS spreads that are lower than the US, I will get a negative number. You can add just this default spread to the mature market premium to arrive at the total equity risk premium. I add an additional step. In the short term especially, the equity country risk premium is likely to be greater than the country's default spread. You can estimate an adjusted country risk premium by multiplying the default spread by the relative equity market volatility for that market (Std dev in country equity market/Std dev in country bond). I have used the emerging market average of 1.12 (estimated by comparing a emerging market equity index to an emerging market government/public bond index) to estimate country risk premium.I have added this to my estimated risk premium of 5.08% for mature markets (obtained by looking at the implied premium for the S&P 500) to get the total risk premium. Notes:  The year of publication has been considered as per publication date. For example, data published on 2018-Jan considered as 2018, similarly 2019-Jan as 2019    

Reports - Statistical Releases E.16 Country Exposure Lending Survey and Country Exposure Information Report

The data in the table concerning country of birth were corrected on 22 May 2019.For some with country of birth data recorded as 'unknown' (2,404 people), some other foreign country has been specified as the country of birth.Description of statistic Quality descriptions Concepts and definitionsStarting from 1750 Population statistics have been digitised into PDF format in the National Library's Doria service: Publications on Population structure and Vital statistics in Doria (in Finnish) Publications on Population censuses in Doria (in Finnish) ---RegionThese statistics apply the regional division of 1 January 2019 to the whole time series.Country of birthCountries (ISO 3166). The used classification of continents is the classification of Eurostat, where Cyprus and Turkey belong to Europe. In the classification of continents, Europe does not include the figures for Finland. Non-autonomous states are combined with the mother country. Sudan = Sudan + Former Sudan --- HDI = Human Development Index Ranking according to the 2016 HDI index by country: The former Soviet Union is included in the Russia HDI category The former Czechoslovakia is included in the Czech Republic HDI category The former Yugoslavia and Serbia and Montenegro are included in the Serbia HDI categoryAgeAge refers to a person's age in whole years as at 31 December.InformationPopulation 31 DecPopulation at the end of the statistical reference period.

Data cited at:  The African Development Bank: Dataset name: Country Policy and Institutional Assessment (CPIA) - https://cpia.afdb.org/?page=data

The country statistical profiles provide a broad selection of indicators, illustrating the demographic, economic, environmental and social developments, for all OECD members. The dataset also covers the five key partner economies with which the OECD has developed an enhanced engagement program with (Brazil, China, India, Indonesia and South Africa) ,accession countries (Colombia, Costa Rica and Lithuania) , Peru and the Russian Federation. The user can easily compare indicators across all countries. Total fertility rates - Unit of measure used: Number of children born to women aged 15 to 49

Data cited at: Our World in data-https://ourworldindata.org/coronavirus-source-data 

The Credit to Agriculture dataset provides national data for over 100 countries on the amount of loans provided by the private/commercial banking sector to producers in agriculture, forestry and fisheries, including household producers, cooperatives, and agro-businesses. For some countries, the three sub sectors of agriculture, forestry, and fishing are completely specified. In other cases, complete dis aggregations are not available. The dataset also provides statistics on the total credit to all industries, indicators on the share of credit to agricultural producers, and an agriculture orientation index (the agriculture share of credit, over the agriculture share of GDP).

Data cited at: International Lesbian, Gay, Trans and Intersex Association-ILGA World 

Monthly data on crude oil imports by country and by oil type in Japan.

Note: (1) Source: Council Regulation (EC) n°2964/95 of 20 December 1995. (2) The cif price includes the fob price (the price actually invoiced at the port of loading), the cost of transport, insurance and certain charges linked to crude oil transfer operations. (3) Due to confidentiality Czech Republic is excluded from EU(28). (4) For Romania November-2016 and December-2016 are estimations derived from Eurostat data

GHG emissions data from cultivation of organic soils are those associated with nitrous oxide gas from cultivated organic soils under cropland (item: cropland organic soils) and grassland (item: grassland organic soils). The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided by country, region and special groups, with global coverage, relative to the period 1990-present (with annual updates) and with projections for 2030 and 2050, expressed both as Gg N2O and Gg CO2eq, by cropland, grassland and by the two aggregated. Implied emission factor for N2O as well activity data (areas) are also provided.

The European Commission provides statistics on EU crude oil imports and crude oil supply costs.

Revisions on these statisticsDescription of statistic Methodological descriptions Concepts and definitions Changes in these statistics... confidentialBOP itemMaksutaseen ja ulkomaisen varallisuusaseman laadinnassa ja taulukoinnissa käytetty int_acc_item on periaatteessa osajoukko taloustoimista. Int_acc_item-luokat on siksi viety osaksi virallista PxPro-taloustoimiluokitusta taloustoimi_1_20180101 ja sitä hyödynnetään tietojen taulukoinnissa.

Data cited at: Statistics Finland  http://www.stat.fi/index_en.html Post: 012 -- Current account by country breakdown http://pxnet2.stat.fi/PXWeb/pxweb/en/StatFin/StatFin__kan__mata/statfin_mata_pxt_012.px License: http://creativecommons.org/licenses/by/4.0/ The releases for September, October and November had an error in the figures for primary income in 2013 and 2014. There was also an error in the flow data on foreign assets and liabilities for 2015. The error was corrected in the December release on 14 December 2018. The statistics become final more than two years after the end of the reference year. Revisions in these statistics Description Methodological descriptions Concepts and definitions

Data cited at: https://cameroon.opendataforafrica.org/vszafdc Demographic Statistics of African countries  

In latest update, the total value of sovereign debt in default at US$443.2 billion in 2020 (0.5 percent of world public debt), up US$143.6 billion (48 percent) from the revised total of US$299.6 billion in 2019. This outpaces the 13 percent increase in gross world public debt. The data by major creditor categories show that the increase was driven mainly by foreign currency bonds in default, which rose by US$121.2 billion. This reflected:new defaults by Argentina, Belize, Ecuador and Surinamea first-time default on foreign currency bonds by Lebanona greater amount of interest arrears from ongoing bond defaults by Venezuela and Puerto Rico Local currency debt in default increased by US$0.9 billion. This was due mainly to Iraq’s restructuring of its short-term debt into debt with longer-term maturities and lower coupon rates.

Direct Investment by Country measures the value of direct investment in the United States by overseas investors and U.S. investment in other countries. This Dataset provides data for a large set of countries broken down by major industry.

U.S. Direct Investment Abroad: Income Without Current-Cost Adjustment, Quarterly Update 

Direct Investment Abroad: Reinvestment of Earnings Without Current Cost Adjustment, United States

The burden of disease is calculated using the disability-adjusted life year (DALY). One DALY represents the loss of the equivalent of one year of full health.

Measles cases are defined as laboratory confirmed, epidemiologically linked, and clinical cases as reported to the World Health Organization.

Data cited at: The African Development Bank: The Middle of the Pyramid: Dynamics of the Middle Class in Africa: https://www.afdb.org/fileadmin/uploads/afdb/Documents/Publications/The%20Middle%20of%20the%20Pyramid_The%20Middle%20of%20the%20Pyramid.pdf

Metainformation:Last changes: data set year 2020 State of the database: April 2021 Reference period: 1st January - 31st December Data source: Vital statistics BEVNAT

1. ccTLDs stands for country code top-level domains. 2. gTLDs - stands for generic top-level domains.

Domestic Demand of Machine Tools in Japan

Prior to 2003, merchandise trade data exclude Singapore's merchandise trade with Indonesia.

Earnings Of Foreign Direct Investment In Singapore By Country/Region (During The Year), Annual

This data set contains information of The statistics yearbook 2017 is compiled and published by Lao statistics Bureau, Ministry of Planning and Investment. Lao statistics Bureau of expressing gratitude provincial statistics Centre, line ministries involved, ministry-equivalent agencies and other parties to contribute to compile statistical data Economic, Social and Natural Resources and Environment cooperation throughout, Lao Statistics Bureau welcome the proposal, comments and feedback to guide the improvement this magazines better. Meanwhile, the Lao Statistics Bureau express thanks to highly among those interested in using information and statistics to Lao Statistics Bureau. TRANSLATE with xEnglishArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian  TRANSLATE with COPY THE URL BELOW BackEMBED THE SNIPPET BELOW IN YOUR SITEEnable collaborative features and customize widget: Bing Webmaster PortalBack

This table contains data on economic short-time workers by professional status (employees or total employment). Economic short-time workers comprise workers who are working less than usual due to business slack, plant stoppage, or technical reasons. However, the definitions are not harmonised which hampers the comparison across countries. Data are broken down professional status - employees, total employment - by sex and by standardised age groups (15-24, 25-54, 55+, total).

This data set contains information of The statistics yearbook 2017 is compiled and published by Lao statistics Bureau, Ministry of Planning and Investment. Lao statistics Bureau of expressing gratitude provincial statistics Centre, line ministries involved, ministry-equivalent agencies and other parties to contribute to compile statistical data Economic, Social and Natural Resources and Environment cooperation throughout, Lao Statistics Bureau welcome the proposal, comments and feedback to guide the improvement this magazines better. Meanwhile, the Lao Statistics Bureau express thanks to highly among those interested in using information and statistics to Lao Statistics Bureau.

Greenhouse Gas (GHG) emissions from burning crop residues consist of methane (CH4) and nitrous oxide (N2O) gases produced by the combustion of a percentage of crop residues burnt on-site. The mass of fuel available for burning should be estimated taking into account the fractions removed before burning due to animal consumption, decay in the field, and use in other sectors (e.g., biofuel, domestic livestock feed, building materials, etc.). FAOSTAT emission estimates are computed at Tier 1 following the IPCC 2006 Guidelines for National GHG Inventories (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided by country, reguions and special groups, with global coverage, relative to the period 1961-present (with annual updates) and with projections for 2030 and 2050, expressed both as Gg CH4, Gg N2O, Gg CO2eq and CO2eq from CH4 and N2O, by crop (maize, rice, sugarcane and wheat) and by aggregates. Implied emission factors for N2O and CH4 as well activity data (biomass burned) are also provided.

Greenhouse gas (GHG) emissions from crop residues consist of direct and indirect nitrous oxide (N2O) emissions from nitrogen (N) in crop residues and forage/pasture renewal left on agricultural fields by farmers. Specifically, N2O is produced by microbial processes of nitrification and de-nitrification taking place on the deposition site (direct emissions), and after volatilization/re-deposition and leaching processes (indirect emissions). The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories, Vol. 4, Ch. 2 and 11(http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided as direct, indirect and total by country, regions and special groups, with global coverage, relative to the period 1961-present (with annual updates) and with projections for 2030 and 2050, expressed as Gg N2O and Gg CO2eq, by crop and N content in residues.

Greenhouse gas (GHG) emissions from enteric fermentation consist of methane gas produced in digestive systems of ruminants and to a lesser extent of non-ruminants. The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories vol. 4, ch. 10 and 11 (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided by country, regions and special groups, with global coverage, relative to the period 1961-present (with annual updates) and with projections for 2030 and 2050, expressed both as Gg CH4 and Gg CO2eq, by livestock species (asses, buffaloes, camels, cattle (dairy and non-dairy), goats, horses, llamas, mules, sheep, swine (breeding and market)) and by species aggregates (all animals, camels and llamas, cattle, mules and asses, sheep and goats, swine). Implied emission factor for CH4 and activity data are also provided

GHG emissions from manure applied to soils consist of direct and indirect nitrous oxide (N2O) emissions from manure nitrogen (N) added to agricultural soils by farmers. Specifically, N2O is produced by microbial processes of nitrification and de-nitrification taking place on the application site (direct emissions), and after volatilization/re-deposition and leaching processes (indirect emissions). The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories vol. 4, ch. 10 and 11 (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided as direct, indirect and total by country, regions and special groups, with global coverage, relative to the period 1961-present (with annual updates) and with projections for 2030 and 2050, expressed as Gg N2O and Gg CO2eq, by livestock species (asses, buffaloes, camels, cattle (dairy and non-dairy), chickens (broilers and layers), ducks, goats, horses, llamas, mules, sheep, swine (breeding and market) and turkeys) and by species aggregates (all animals, camels and llamas, cattle, chickens, mules and asses, poultry birds, sheep and goats, swine). Implied emission factor for N2O and activity data (N content in manure) are also provided.

GHG emissions from manure left on pastures consist of direct and indirect nitrous oxide (N2O) emissions from manure nitrogen (N) left on pastures by grazing livestock. Specifically, N2O is produced by microbial processes of nitrification and de-nitrification taking place on the deposition site (direct emissions), and after volatilization/re-deposition and leaching processes (indirect emissions). The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories vol. 4, ch. 10 and 11 (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided by country, regions and special groups, with global coverage, relative to the period 1961-present (with annual updates) and with projections for 2030 and 2050, expressed as direct, indirect and total Gg N2O and Gg CO2eq, by livestock species (asses, buffaloes, camels, cattle (dairy and non-dairy), chickens (broilers and layers), ducks, goats, horses, llamas, mules, sheep, swine (breeding, market), turkeys) and by species aggregates (all animals, camels and llamas, cattle, chickens, mules and asses, poultry birds, sheep and goats, swine). Implied emission factor for N2O and N content in manure are also provided.

Greenhouse gas (GHG) emissions from synthetic fertilizers consist of nitrous oxide gas from synthetic nitrogen additions to managed soils. Specifically, N2O is produced by microbial processes of nitrification and de-nitrification taking place on the addition site (direct emissions), and after volatilization/re-deposition and leaching processes (indirect emissions). The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories vol. 4, ch. 11 (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided as direct, indirect and total by country, regions and special groups, with global coverage, relative to the period 1961-present (with annual updates) and with projections for 2030 and 2050, expressed as Gg N2O and Gg CO2eq. Implied emission factor for N2O and activity data (consumption) are also provided.

Greenhouse Gas (GHG) emissions from burning of biomass consist of methane and nitrous oxide gases from biomass combustion of forest land cover classes ‘Humid and Tropical Forest’ and ‘Other Forests’, and of methane, nitrous oxide, and carbon dioxide gases from combustion of organic soils. The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided by country, with global coverage, relative to the period 1990-present (with annual updates), expressed as Gg CH4, Gg N2O, Gg CO2, Gg CO2eq and Gg CO2eq from both CH4 and N2O, by land cover class (humid tropical forest, other forest, organic soils) and by aggregate (burning - all categories). Implied emission factors for N2O, CH4 and CO2 as well activity data (burned area and biomass burned) are also provided.

Annual net CO2 emission/removal from Forest Land consist of net carbon stock gain/loss in the living biomass pool (aboveground and belowground biomass) associated with Forest and Net Forest Conversion. The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories (http://www.ipcc-nggip.iges.or.jp/public/2006gl/index.html) and using area and carbon stocks data compiled by countries in the FAO Global Forest Resource Assessments (http://www.fao.org/forestry/fra/en/). GHG emissions are provided by country, regions and special groups, with global coverage, relative to the period 1990-present (with annual updates), expressed as net stock change Gg C, net emissions/removals Gg CO2 and CO2eq, by forest or net forest conversion and by aggregate (forest land). Implied emission factor for CO2 as well as activity data (area, net area difference, total forest area and carbon stock in living biomass) are also given.

The FAOSTAT domain Organic soils consist of nitrous oxide (N2O) and carbon dioxide (CO2) emissions associated with the drainage of organic soils ? using histosols as proxy ? for agriculture. Data are computed geospatially, using the Tier 1 method of the 2006 IPCC Guidelines for National greenhouse gas Inventories (IPCC, 2006). Estimates are available by country and with global coverage, in complete time series for the period 1990?2019. The database is updated annually. The FAOSTAT domain Organic soils disseminates information by country on: activity data (in hectares of organic soils drained for agriculture); and greenhouse gas (GHG) emissions (in kilotonnes of N2O and CO2). Drainage and associated emissions are assessed separately for IPCC land use categories cropland and grassland, corresponding to FAO land use categories ??cropland?? and ??permanent meadows and pastures.?? Data are available for all countries and territories, for standard FAOSTAT regional aggregations, and for Annex I and non-Annex I country groups. This FAOSTAT domain also disseminates the activity data and emissions data reported by countries to the United Nations Framework Convention on Climate Change (UNFCCC), under ?Cultivation of organic soils? for the N2O component and categories ?Cropland drained organic soils? and ?Grassland drained organic soils? for the CO2. Activity data are sourced from the most recently available GHG National Inventories (NGHGI) or from National Communications. Emission data are sourced directly from the UNFCCC data portal or from Biennial Update Reports (BURs). UNFCCC data are disseminated in FAOSTAT with permission, formalized via a FAO-UNFCCC Memorandum of Understanding.

As of Jun 2021, estimates of greenhouse gas (GHG) emissions from agriculture and land use are disseminated under the FAOSTAT heading Climate Change/Emissions. This heading consolidates and replaces two previous distinct headings: Emissions-Agriculture and Emissions-Land Use, which no longer exist. Several other domains were consolidated to facilitate data access. https://fenixservices.fao.org/faostat/static/documents/GT/README_Changes_in_2021.pdf   The domain Emissions Totals consolidatesin one single dataset the information previously presented in two separate datasets: Agriculture total (previously: http://www.fao.org/faostat/en/#data/GT; containing data on non-CO2 emissions from agriculture); and Land Use total (previously: http://www.fao.org/faostat/en/#data/GL; containing CO2 and non-CO2 emissions from land use, land use change and forestry. This link no longer works).

For more information, refer to the International Labour Migration Statistics (ILMS) database description.

Employed migrants refer to the number of persons who changed their country of usual residence and were also employed during a specified brief period. Data are disaggregated by country of origin. A person's country of origin is that from which the person originates, i.e. the country of his or her citizenship (or, in the case of stateless persons, the country of usual residence).

For more information, refer to the International Labour Migration Statistics (ILMS) database description.

Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILO estimates and projections methodological note.

Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILO estimates and projections methodological note.

This table contains data on permanent and temporary workers based on the type of work contract of their main job. Data are further broken down by professional status - employees, total employment - by sex and by standardised age groups (15-19, 15-24, 20-24, 25-54, 55-64, 65+, total). Unit of measure used - Data are expressed in thousands of persons.

Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). The series is part of the ILO modelled estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILOSTAT pages on concepts and definitions and ILO modelled estimates and projections.

Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. The employed comprise all persons of working age who, during a specified brief period, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by economic activity, which refers to the main activity of the establishment in which a person worked during the reference period. The series is part of the ILO modelled estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILOSTAT pages on concepts and definitions and ILO modelled estimates and projections.

Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. The employed comprise all persons of working age who, during a specified brief period, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by occupation according to the latest version of the International Standard Classification of Occupations (ISCO). The series is part of the ILO modelled estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILOSTAT pages on concepts and definitions and ILO modelled estimates and projections.

Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by status in employment according to the latest version of the International Standard Classification of Status in Employment (ICSE-93). Status in employment refers to the type of explicit or implicit contract of employment the person has with other persons or organizations. The basic criteria used to define the groups of the classification are the type of economic risk and the type of authority over establishments and other workers which the job incumbents have or will have. The series is part of the ILO modelled estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILOSTAT pages on concepts and definitions and ILO modelled estimates and projections.

Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. The series is part of the ILO modelled estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILOSTAT pages on concepts and definitions and ILO modelled estimates and projections.

The employed comprise all persons of working age who, during a specified brief period, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by economic activity, which refers to the main activity of the establishment in which a person worked during the reference period and does not depend on the specific duties or functions of the person's job, but on the characteristics of the economic unit in which this person works. The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. Data for 1991-2016 are estimates while 2017-2021 data are projections. The dataset was updated as of November 2017. For more information, refer to the indicator description and the ILO estimates and projections methodological note.  

The employed comprise all persons of working age who, during a specified brief period, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by occupation according to the latest version of the International Standard Classification of Occupations (ISCO). Information on occupation provides a description of the set of tasks and duties which are carried out by, or can be assigned to, one person. The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. Data for 1991-2016 are estimates while 2017-2021 data are projections. The dataset was updated as of November 2017. For more information, refer to the indicator description and the labour force estimates and projections methodological paper.   

Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILO estimates and projections methodological note.

Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. The employment-to-population ratio expresses the number of persons who are employed as a percent of the total working age population. The series is part of the ILO modelled estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILOSTAT pages on concepts and definitions and ILO modelled estimates and projections.

BGR provides the biennial energy study which covers data and developments concerning German and Global energy supplies. In addition the study also provides information on the global generation of renewable energies, including geothermal energy and hydrogen. 

Data is collected from central or federal government budgets, as well as the budgets of state-owned companies, for spending on a range of sectors including energy efficiency, renewables, nuclear power, fossil fuels, hydrogen and fuel cells, and more. All figures refer to total public energy RD&D expenditure data, converted from current prices in national currencies to US dollar PPPs in constant 2022 prices, using GDP deflators and 2022 PPPs. Note- 2022 and 2023 data are estimation by the countries

Number of students enrolled in different education programmes by country of origin and sex.

Environmentally related taxes are effective policy instruments to shape relative prices of goods and services. The Environmental Related Tax Revenue Database (ERTR) categorises taxes based on their environmental relevance, constructing environmentally related tax revenue with a breakdown by tax-base category (including energy, transport, pollution, and resources) and 22 environmental domains. Note that tax-base categories are mutually exclusive, while domains are not. Therefore, one should not aggregate revenue across domains as it may lead to double counting.

Sources: OECD, World Bank, NBS China

The estimated resident population (ERP) is the official measure of the Australian population. This dataset contains annual ERP by country of birth, age and sex at the Australia level. At the state/territory level it is available for Census years only.

>The Securities Industry and Financial Markets Association (SIFMA) prepared this material for informational purposes only. SIFMA obtained this information from multiple sources believed to be reliable as of the date of publication; SIFMA, however, makes no representations as to the accuracy or completeness of such third party information. SIFMA has no obligation to update, modify or amend this information or to otherwise notify a reader thereof in the event that any such information becomes outdated, inaccurate, or incomplete. >The Securities Industry and Financial Markets Association (SIFMA) brings together the shared interests of hundreds of securities firms, banks and asset managers. SIFMA's mission is to support a strong financial industry, investor opportunity, capital formation, job creation and economic growth, while building trust and confidence in the financial markets. SIFMA, with offices in New York and Washington, D.C., is the U.S. regional member of the Global Financial Markets Association (GFMA). For more information, visit www.sifma.org.

Dataset includes European Economic economic forecast releases from Autumn 2017 through Winter 2023.

Insurance Europe has published a database with figures on the European insurance industry for the period 2004 to 2017. The database includes information on insurers’ investment portfolio, benefits and claims paid, distribution channels and the market structure for the life, non-life and health insurance sectors in Europe.

Note: For latest data, please contact us: https://knoema.com/contact  

This dataset contains the exchange rate of 1USD to their country local currency.

Recent exchange rate movements have been unusually large, triggering a debate regarding their likely effects on trade. Historical experience in advanced and emerging market and developing economies suggests that exchange rate movements typically have sizable effects on export and import volumes. A 10 percent real effective depreciation in an economy’s currency is associated with a rise in real net exports of, on average, 1.5 percent of GDP, with substantial cross-country variation around this average. Although these effects fully materialize over a number of years, much of the adjustment occurs in the first year. The boost to exports associated with currency depreciation is found to be largest in countries with initial economic slack and with domestic financial systems that are operating normally. Some evidence suggests that the rise of global value chains has weakened the relationship between exchange rates and trade in intermediate products used as inputs into other economies’ exports. However, the bulk of global trade still consists of conventional trade, and there is little evidence of a general trend toward disconnect between exchange rates and total exports and imports.

This collection covers national tourism.  Data is collected by the competent national authorities of the Member States and is compiled according to a harmonised methodology established by EU regulations before transmission to Eurostat. Most of the time, data on domestic and outbound trips (where "outbound tourism" means residents of a country travelling in another country) is collected via sample surveys. However, in a few cases the data is compiled from border surveys. Surveys are generally conducted on a monthly or quarterly basis.   The concepts and definitions used in the collection of data shall conform to the specifications described in the Methodological manual for tourism statistics.   With the entry into force of the Regulation (EU) 692/2011 of the European Parliament and of the Council, Member States are transmitting microdata to Eurostat, which enables that data to be disseminated far more widely (since reference period 2012).   The information on tourism demand, concern trips (for the population aged 15 years and over) of which the main purpose is holidays or business and which involve at least one or more consecutive nights spent away from the usual place of residence (See annex at the bottom of the page).   Aggregated data on participation in tourim is also transmitted to Eurostat and covers the resident population aged 15 or over, participating in tourism for for personal purpose during the reference year.   Microdata on trips of EU residents as well as participation data are transmitted to Eurostat one time per year. Data are disseminated when they respect agreed validation rules and other quality criteria.

This collection covers national tourism.  Data is collected by the competent national authorities of the Member States and is compiled according to a harmonised methodology established by EU regulations before transmission to Eurostat. Most of the time, data on domestic and outbound trips (where "outbound tourism" means residents of a country travelling in another country) is collected via sample surveys. However, in a few cases the data are compiled from border surveys. Surveys are generally conducted on a monthly or quarterly basis.   The concepts and definitions used in the collection of data shall conform to the specifications described in the Methodological manual for tourism statistics. With the entry into force of the Regulation (EU) 692/2011 of the European Parliament and of the Council, Member States are transmitting microdata to Eurostat, which enables that data to be disseminated far more widely (since reference period 2012). The information on tourism demand concerns trips (for the population aged 15 years and over) of which the main purpose is holidays or business and which involve at least one or more consecutive nights spent away from the usual place of residence (See annex at the bottom of the page). Aggregated data on participation in tourim is also transmitted to Eurostat and covers the resident population aged 15 or over, participating in tourism for personal purpose during the reference year. Microdata on trips of EU residents as well as participation data are transmitted to Eurostat one time per year. Data are disseminated when they respect agreed validation rules and other quality criteria.

Statistics Name: Current Survey of Supply and Demand for Petroleum Products Annual Report Petroleum Products Import and Export of Petroleum Products/Monthly Export by Area and Country F.Y.

This table presents annual statistics on international trade in services of individual economies by some 100 service categories for trade-partner “World total”. It further contains aggregated figures for various groups of economies for selected principal service categories. The data presented are the result of the common work of UNCTAD and the World Trade Organization (WTO).

Exports by Country for Senegal

Air pollution is considered one of the most pressing environmental and health issues across OECD countries and beyond. According to the World Health Organisation (WHO), exposure to fine particulate matter (PM2.5) has potentially the most significant adverse effects on health compared to other pollutants. PM2.5 can be inhaled and cause serious health problems including both respiratory and cardiovascular disease, having its most severe effects on children and elderly people. Exposure to PM2.5 has been shown to considerably increase the risk of heart disease and stroke in particular. For these reasons, population exposure to (outdoor or ambient) PM2.5 has been identified as an OECD Green Growth headline indicator. The underlying PM2.5 concentrations estimates are taken from van Donkelaar et al. (2016). They have been derived using satellite observations and a chemical transport model, calibrated to global ground-based measurements using Geographically Weighted Regression at 0.01° resolution. The underlying population data, Gridded Population of the World, version 4 (GPWv4) are taken from the Socioeconomic Data and Applications Center (SEDAC) at the NASA. The underlying boundary geometries are taken from the Global Administrative Unit Layers (GAUL) developed by the FAO, and the OECD Territorial Classification, when available. The current version of the database presents much more variation with respect to the previous one. The reason is that the underlying concentration estimates previously included smoothed multi-year averages and interpolations; while in the current version annual concentration estimates are used. Establishing trends of pollution exposure should be done with care, especially at smaller output areas, as their inputs (e.g. underlying data and models) can change from year to year. We recommend using a 3-year moving average for visualisation.

If a search in the StatBank does not return any result, this does not necessarily mean that there is no trade or that the country code is not valid in the particular period. A zero (0) could also imply that the figures are confidential or that the value is less than half of the unit used. From 2006 on the following countries had their belonging to continent changed - Cyprus from Asia to Europe, and Armenia, Georgia, Kyrgyzstan, Kazakhstan, Tajikistan, Turkmenistan and Uzbekistan from Europe to Asia. The total for Trade are/continent includes this change - while in the selections of countries in the pull-down menu for European/Asian countries it is the present classification that will be shown for the whole time period (Cypros belonging to Europe and the other countries to Asia). Statistics Norway do not publish figures for the trade region previously Comecon after 2011. For more information, see About the statisticsMonthly figures are released on the 15th of the month after the observation period (the previous month), or the first subsequent working day. These figures are preliminary. Corresponding yearly figures are published together with the monthly figures for December. With regard to the current year, all the monthly figures are updated in every publication. Final figures for the preceding year are released twice. For the first time in May the following year, while the corrected final figures are published in May one year later.countryEUCroatia is included in the trade with the EU from 2014 on.Palestine (2013-)Previously: West Bank/Gaza Stripe (2001-2012)

If a search in the StatBank does not return any result, this does not necessarily mean that there is no trade or that the country code is not valid in the particular period. A zero (0) could also imply that the figures are confidential or that the value is less than half of the unit used. From 2006 on the following countries had their belonging to continent changed - Cyprus from Asia to Europe, and Armenia, Georgia, Kyrgyzstan, Kazakhstan, Tajikistan, Turkmenistan and Uzbekistan from Europe to Asia. The total for Trade are/continent includes this change - while in the selections of countries in the pull-down menu for European/Asian countries it is the present classification that will be shown for the whole time period (Cypros belonging to Europe and the other countries to Asia). Statistics Norway do not publish figures for the trade region previously Comecon after 2011. For more information, see About the statisticsMonthly figures are released on the 15th of the month after the observation period (the previous month), or the first subsequent working day. These figures are preliminary. Corresponding yearly figures are published together with the monthly figures for December. With regard to the current year, all the monthly figures are updated in every publication. Final figures for the preceding year are released twice. For the first time in May the following year, while the corrected final figures are published in May one year later.countryEUCroatia is included in the trade with the EU from 2014 on.Palestine (2013-)Previously: West Bank/Gaza Stripe (2001-2012)

The table shows the 10 main partners of the EU27 (from 2020) for that product group (according to the sum imports+exports). Imports are expressed in value terms and measured cif (cost, insurance, freight). Exports are expressed in value terms and measured fob (free on board). Balance = export - import.

The table shows the 10 main partners of the EU27 (from 2020) for that product group (according to the sum imports+exports). Imports are expressed in value terms and measured cif (cost, insurance, freight). Exports are expressed in value terms and measured fob (free on board). Balance = export - import.

The table shows the 10 main partners of the EU27 (from 2020) for that product group (according to the sum imports+exports). Imports are expressed in value terms and measured cif (cost, insurance, freight). Exports are expressed in value terms and measured fob (free on board). Balance = export - import.

The table shows the 10 main partners of the EU27 (from 2020) for that product group (according to the sum imports+exports). Imports are expressed in value terms and measured cif (cost, insurance, freight). Exports are expressed in value terms and measured fob (free on board). Balance = export - import.

The table shows the 10 main partners of the EU27 (from 2020) for that product group (according to the sum imports+exports). Imports are expressed in value terms and measured cif (cost, insurance, freight). Exports are expressed in value terms and measured fob (free on board). Balance = export - import.

The table shows the 10 main partners of the EU27 (from 2020) for that product group (according to the sum imports+exports). Imports are expressed in value terms and measured cif (cost, insurance, freight). Exports are expressed in value terms and measured fob (free on board). Balance = export - import.

The table shows the 20 main partners of the EU27 (from 2020) (according to the sum imports+exports), expressed in value terms and in % of the total flow.

The dataset includes data on gross and net production indices for various food and agriculture aggregates expressed in both totals and per capita.

Note: Ex: <0.1,<0.2,<0.3 considered as 0.1,0.2,0.3 values  

Value of gross production has been compiled by multiplying gross production in physical terms by output prices at farm gate. Thus, value of production measures production in monetary terms at the farm gate level. Since intermediate uses within the agricultural sector (seed and feed) have not been subtracted from production data, this value of production aggregate refers to the notion of "gross production". Value of gross production is provided in both current and constant terms and is expressed in US dollars and Standard Local Currency (SLC). The current value of production measures value in the prices relating to the period being measured. Thus, it represents the market value of food and agricultural products at the time they were produced. Knowing this figure is helpful in understanding exactly what was happening within a given economy at that point in time. Often, this information can help explain economic trends that emerged in later periods and why they took place. Value of production in constant terms is derived using the average prices of a selected year or years, known as the base period. Constant price series can be used to show how the quantity or volume of products has changed, and are often referred to as volume measures. The ratio of the current and constant price series gives a measure of price movements. US dollar figures for value of gross production are converted from local currencies using official exchange rates as prevailing in the respective years. The SLC of a country is the local currency prevailing in the latest year. Expressing data series in one uniform currency is useful because it avoids the influence of revaluation in local currency, if any, on value of production

The Fertilizers by Product dataset contains information on product amounts for the Production, Trade, Agriculture Use and Other Uses of chemical and mineral fertilizers products, over the time series 2002-present. The fertilizer statistics data are validated separately for a set of over thirty individual products. Both straight and compound fertilizers are included.

This dataset FDI flows by economic activity, BMD4 includes inward and outward Foreign Direct Investment (FDI) flows by economic activity for OECD reporting economies: Inward FDI flows by economic activity measure the value of cross-border direct investment transactions received by the reporting economy in a specific industry during a year, enabling, for example, the identification of the most attractive industries for FDI in each OECD economy in that year. Outward FDI flows by economic activity measure the value of cross-border direct investment transactions from the reporting economy during a year, by industry. The industry sector corresponds to the activity of the direct investment enterprise or to the activity of the direct investor (more details on the activity allocation method for outward FDI flows are indicated in the metadata information attached at the country level). Inward and outward FDI flows by economic activity are presented according to the directional principle (unless otherwise specified in the country level metadata); they are measured in USD millions and in millions of national currency. A cross-classification of inward and outward FDI flows by major ISIC4 sections and by main geographic aggregates are also available for some OECD reporting economies.

This dataset FDI flows main aggregates, BMD4 is updated every quarter and includes annual and quarterly aggregate Foreign Direct Investment (FDI) flows for OECD member countries and for non-OECD G20 countries (Argentina, Brazil, China, India, Indonesia, Saudi Arabia and South Africa), which are included in Balance of Payments (BOP) accounts. FDI flows record the value of cross-border transactions related to direct investment during a given period of time, usually a quarter or a year, and consist of equity transactions, reinvestment of earnings, and intercompany debt transactions.

This dataset FDI income by economic activity, BMD4 includes Foreign Direct Investment (FDI) payments and receipts by economic activity for OECD reporting economies: FDI income payments (or income on inward FDI) by economic activity measure the total returns within a year on direct investment stocks paid by enterprises in the reporting economy, in a specific industry, to their foreign investors. FDi income receipts (or income on outward FDI) by economic activity measure the total returns within a year on direct investment stocks received by investors in the reporting economy from their direct investment enterprises abroad, by industry. The industry sector corresponds to the activity of the direct investment enterprise or to the activity of the direct investor (more details on the activity allocation method for FDI income receipts are indicated in the metadata information attached at the country level). Income payments and receipts (or income on inward and outward FDI respectively) by economic activity are presented according to the directional principle (unless otherwise specified in the country level metadata); they are measured in USD millions and in millions of national currency.

This dataset FDI income main aggregates, BMD4 is updated every quarter and includes annual and quarterly aggregate Foreign Direct Investment (FDI) income for OECD member countries and for non-OECD G20 countries (Argentina, Brazil, China, India, Indonesia, Saudi Arabia and South Africa), which are included in Balance of Payments (BOP) accounts.

This dataset FDI main aggregates, BMD4 is updated every quarter and includes quarterly and annual aggregate inward and outward Foreign Direct Investment (FDI) flows, positions and income for OECD reporting economies and for non-OECD G20 countries (Argentina, Brazil, China, India, Indonesia, Saudi Arabia and South Africa). It is a simplified dataset with fewer breakdowns compared to the other separate datasets specifically dedicated to FDI flows, FDI positions or FDI income aggregates. In this dataset, FDI statistics are presented on directional basis only (unless otherwise specified, see metadata attached at the reporting country level) and resident Special Purpose Entities (SPEs), when they exist, are excluded (unless otherwise stated, see metadata attached at the reporting country level). FDI aggregates are measured in USD millions, in millions of national currency and as a share of GDP.

This dataset FDI positions main aggregates, BMD4 is updated every quarter and includes annual aggregate Foreign Direct Investment (FDI) positions for OECD member countries and for non-OECD G20 countries (Argentina, Brazil, China, India, Indonesia, Saudi Arabia and South Africa), which are included in International Investment Position (IIP) accounts. FDI positions record the total level of direct investment at a given point in time, usually the end of a quarter or of a year.

Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. The labour force comprises all persons of working age who furnish the supply of labour for the production of goods and services during a specified time-reference period. It refers to the sum of all persons of working age who are employed and those who are unemployed. The working-age population is commonly defined as persons aged 15 years and older, but this varies from country to country. The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILO estimates and projections methodological note.

cThe dataset contains data on Import and Export Value (expressed in 1000 USD) for a selected list of fertilizers, from 1961 on wards. Country and country aggregate data are available. The fertilizers covered are: Nitrogenous fertilizers; Phosphate fertilizers; Potash fertilizers; Fertilizers Manufactured, nes; Fertilizers, Organic; Natural Phosphates; Natural Potassic Salts; Natural Sodium Nitrate.

Data on causes of death (COD) provide information on mortality patterns and form a major element of public health information. COD data refer to the underlying cause which - according to the World Health Organisation (WHO) - is "the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury". Causes of death are classified by the 86 causes of the "European shortlist" of causes of death. This shortlist is based on the International Statistical Classification of Diseases and Related Health Problems (ICD). COD data are derived from death certificates. The medical certification of death is an obligation in all Member States. Countries code the information provided in the medical certificate of cause of death into ICD codes according to the rules specified in the ICD. Data are broken down by sex, 5-year age groups, cause of death and by residency and country of occurrence. For stillbirths and neonatal deaths additional breakdows might include age of mother. Data are available for EU-28, the former Yugoslav Republic of Macedonia, Albania, Iceland, Norway, Liechtenstein and Switzerland. Regional data (NUTS level 2) are available for most of the countries. Annual national data are provided in absolute number, crude death rates and standardised death rates. At regional level (NUTS level 2) the same is provided in form of 3 years averages. Annual crude death rates are also available at NUTS level 2.

Data on causes of death (COD) provide information on mortality patterns and form a major element of public health information. COD data refer to the underlying cause which - according to the World Health Organisation (WHO) - is "the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury". Causes of death are classified by the 86 causes of the "European shortlist" of causes of death. This shortlist is based on the International Statistical Classification of Diseases and Related Health Problems (ICD). COD data are derived from death certificates. The medical certification of death is an obligation in all Member States. Countries code the information provided in the medical certificate of cause of death into ICD codes according to the rules specified in the ICD. Data are broken down by sex, 5-year age groups, cause of death and by residency and country of occurrence. For stillbirths and neonatal deaths additional breakdows might include age of mother. Data are available for EU-28, the former Yugoslav Republic of Macedonia, Albania, Iceland, Norway, Liechtenstein and Switzerland. Regional data (NUTS level 2) are available for most of the countries. Annual national data are provided in absolute number, crude death rates and standardised death rates. At regional level (NUTS level 2) the same is provided in form of 3 years averages. Annual crude death rates are also available at NUTS level 2.

Statistics on growth of the cruise line industry.

Data cited at: Statistics Finland http://www.stat.fi/index_en.html Publication: 004 -- International trade in services by region, 1 000 000 euros http://pxnet2.stat.fi/PXWeb/pxweb/en/StatFin/StatFin__kan__tpulk/statfin_tpulk_pxt_004.px License: http://creativecommons.org/licenses/by/4.0/ The statistics on international trade in goods and services cover international trade in balance of payments terms on the quarterly level. The statistics form a link for goods trade in customs and balance of payments terms, describe the breakdown of quarterly trade in services, and indicate the total exports of goods and services by area. . = Category not applicable. .. = Data not available or too uncertain for presentation, or subject to secrecy. Description of statistics Concepts and definitionsRegion Region and statesYear Year.Data Import The value of imports, 1 000 000 euros.Export The value of exports, 1 000 000 euros.

Data cited at: Statistics Finland http://www.stat.fi/index_en.html Publication: 008 -- Nationality according to age and sex by region in 1990 to 2017 http://pxnet2.stat.fi/PXWeb/pxweb/en/StatFin/StatFin__vrm__vaerak/statfin_vaerak_pxt_008.px License: http://creativecommons.org/licenses/by/4.0/ Concepts and definitions Description Quality description These statistics apply the regional division of 1 January 2018 to the whole time series. Population statistics from 1750 to 2000 have been digitised into PDF format in the National Library's Doria service. Publications on Population structure in Doria (in Finnish) Publications on Vital statistics in Doria (in Finnish) Publications on Population censuses in Doria (in Finnish)Nationality If a person has two nationalities and one of them is Finnish, he/she will be included in statistics as a Finnish national. The used classification of continents is the classification of Eurostat, where Cyprus and Turkey belong to Europe. Citizens of non-autonomous states are summed under the mother country.Nationality Czech Republic Czech Republic + Former CzechoslovakiaSudan Sudan + Former Sudan

Data cited at: Statistics Finland  http://www.stat.fi/index_en.html Publication: 009 -- Finnish citizens with dual nationality by age and second nationality in 2000 to 2017 http://pxnet2.stat.fi/PXWeb/pxweb/en/StatFin/StatFin__vrm__vaerak/statfin_vaerak_pxt_009.px License: http://creativecommons.org/licenses/by/4.0/ Concepts and definitions Description Quality description Population statistics from 1750 to 2000 have been digitized into PDF format in the National Library's Doria service. Publications on Population structure in Doria (in Finnish) Publications on Vital statistics in Doria (in Finnish) Publications on Population censuses in Doria (in Finnish) second nationality If a person has two nationalities and one of them is Finnish, he/she will be included in statistics as a Finnish national. second nationality   Czech Republic Czech Republic + Former Czechoslovakia Sudan Sudan + Former Sudan

Starting from 1750 Population statistics have been digitised into PDF format in the National Library's Doria service: Publications on Population structure and Vital statistics in Doria (in Finnish) Publications on Population censuses in Doria (in Finnish) --- Description of statistic Quality descriptions Concepts and definitionsSecond nationalityCountries (ISO 3166). The used classification of continents is the classification of Eurostat, where Cyprus and Turkey belong to Europe. In the classification of continents, Europe does not include the figures for Finland. Non-autonomous states are combined with the mother country. Czech Republic = Czech Republic + Former Czechoslovakia Sudan = Sudan + Former Sudan If a person has two nationalities and one of them is Finnish, Finnish is as his/her first nationality in statistics. --- HDI = Human Development Index Ranking according to the 2016 HDI index by country: The former Soviet Union is included in the Russia HDI category The former Czechoslovakia is included in the Czech Republic HDI category The former Yugoslavia and Serbia and Montenegro are included in the Serbia HDI categoryAgeAge refers to a person's age in whole years as at 31 December.InformationFinnish citizens with dual nationalityFinnish citizens with dual nationality who are included in the Finnish population on 31 December

First Year Expenditures and Planned Total Expenditures for Investments Initiated in 2019, Country of UBO by Type of Investment

This database contains capture production statistics by country or territory, species item, and FAO Major Fishing Area.

Quarterly flow statistics on direct investment by type, principle, item, country, domestic economic activity and time

Food Balance Sheet presents a comprehensive picture of the pattern of a country's food supply during a specified reference period. The food balance sheet shows for each food item - i.e. each primary commodity and a number of processed commodities potentially available for human consumption - the sources of supply and its utilization. The total quantity of foodstuffs produced in a country added to the total quantity imported and adjusted to any change in stocks that may have occurred since the beginning of the reference period gives the supply available during that period. On the utilization side a distinction is made between the quantities exported, fed to livestock, used for seed, put to manufacture for food use and non-food uses, losses during storage and transportation, and food supplies available for human consumption. The per caput supply of each such food item available for human consumption is then obtained by dividing the respective quantity by the related data on the population actually partaking of it. Data on per caput food supplies are expressed in terms of quantity and - by applying appropriate food composition factors for all primary and processed products - also in terms of caloric value and protein and fat content.

Food Security Measures for IFSA Countries, 2021 and 2031

The Resources and Energy Quarterly (REQ) contains the Office of the Chief Economist’s forecasts for the value, volume, and price of Australia’s major resources and energy commodity exports.

'na' denotes 'not available' or 'not applicable'. '-' denotes 'nil' or 'negligible' or 'not significant'.

Description of statisticQuality descriptions Concepts and definitions... confidential

Description of statisticQuality descriptions Concepts and definitions... confidential

Foreign Direct Investment, Sector Wise in India

Foreign Direct Investment In Singapore By Country/Region And Major Industry (Stock As At Year-End), Annual 'na' denotes 'not available' or 'not applicable'. '-' denotes 'nil' or 'negligible' or 'not significant'. The industries are classified according to Singapore Standard Industrial Classification 2015 (Version 2018).

'na' denotes 'not available' or 'not applicable'. '-' denotes 'nil' or 'negligible' or 'not significant'.

The activities of multinational enterprises statistics available here provide a picture of the overall activities of US affiliates of foreign parents and contain a wide variety of indicators of their financial structure and operations. These statistics cover items that are needed in analyzing the characteristics, performance, and economic impact of MNEs, and are obtained from mandatory surveys of US affiliates of foreign parents conducted by BEA.

Data cited at: https://data.gov.om/bggdftf Foreign Investment

Investment Statistics of Myanmar

Eurostat Dataset Id:educ_mofo_orig Statistics on student/graduate mobility and foreigners in tertiary education collected through the UOE data collection on education and training systems. Data concerning mobility and foreigners are collected as follows:Number of mobile and foreign enrolled students by level of education, programme destination and field of education.Number of mobile and foreign enrolled students by level of education, programme destination, EU/non EU/unknown membership and gender.Number of foreign enrolled students by level of education, programme destination and country of citizenship.Number of mobile enrolled students by level of education, programme destination, and country of origin (usual residence and/or country of prior education).Number of mobile and foreign graduates by level of education, programme destination, cumulative duration and gender. Data by both country of origin and country of destination of the students are disseminated. The absolute figures along with indicators are published.

International trade in services, yearly by imports and exports, items, country and time, Denmark

Merchandise Foreign Trade Statistics of Abu Dhabi

Direction of Trade of Ethiopia. Note: Date 2018/19 took as 2019 while considering quarterly dates. 

International Merchandise Trade Statistics

Sector: External Sector, Sub-sector: Trade

Note: 1) Country-wise data on imports for the year 1999-2000 do not add up to total imports on account of revision in the total imports. 2) Data for European Union exclude UK from 2020-21 onwards.

Note: For latest Year, 2022 value is April to October.

Foreign Trade, Iran

   

Balance of Visible Trade showed a deficit of Rs 11,630 million in October 2021, higher by 0.2% compared to theprevious month and by 41.0% compared to the corresponding month of 2020.In October 2021, total exports decreased by 6.2% compared to September 2021 but remained at same level of October 2020In October 2021, total imports decreased by 2.4% compared to September 2021 and increased by 21.7% compared to October 2020.

Foreign Trade Statistics of Myanmar

Nigeria Trade Statistics by Region and Major Trading Partners  

Data cited at: https://data.gov.om/OMFRTRD2016 this Data set covers the statistical indicators illustrating the development of trade between Oman and other countries, and classification of  merchandise exports, re-exports and merchandise imports by commodity group, nature of materials, their final utilization and port of entry.  It includes also a table on of the balance of payments estimates.   The commodity classification used in the presentation of foreign trade data is the Hormonised System, which has been adopted in Oman since 1987, in addition to the SITC Revision (4) for international comparison.  Commodity values are estimated in Rial Omani on the basis of the (C.I.F.) value for imports (i.e. the cost, insurance and freight of goods to the custom points in Oman) and (F.O.B.) for exports and re-exports.

Qatar: Foreign Merchandise Trade The foreign trade data reflects a clear image of the stages of economical growth in the State of Qatar, as it shows the commodities flow in the shape of national exports, re-exports, and imports to / from different countries of the world. The foreign trade tables contains detailed data for visible imports by country of origin and Exports and Re-Exports by country of destination. The commodity tables are classified by sections and items of the Harmonized System (H.S.) issued by World Customs Organization, which was adapted according to GCC’s needs to meet the actual movement of foreign trade in the region, in addition to other statistical tables. The customs declaration entry issued by Customs Department is the main source of statistical information according to the Special Trade System adopted by the State of Qatar.

Data cited at: Saudi Arabian Monetary Agency-SAMA

For more information, refer to the International Labour Migration Statistics (ILMS) database description.

The database contains data on the production and trade in roundwood and primary wood and paper products for all countries and territories in the world. The main types of primary forest products included in are: roundwood, sawnwood, wood-based panels, pulp, and paper and paperboard. These products are detailed further. The definitions are available. The database contains details of the following topics: - Roundwood removals (production) by type of wood and assortment - Production and trade in roundwood, woodfuel and other basic products - Industrial roundwood by assortment and species - Sawnwood, panels and other primary products - Pulp and paper & paperboard. More detailed information on wood products, including definitions, can be found at http://www.fao.org/forestry/statistics/80572/en/

This table contains data on full-time and part-time employment based on a common definition of 30-usual weekly hours of work in the main job. Data are broken down by professional status - employees, total employment - sex and standardised age groups (15-24, 25-54, 55+, total). Unit of measure used - Data are expressed in thousands of persons.

This dataset contains incidences and gender composition of part-time employment with standardised age groups (15-24, 25-54, 55-64, 65+, total). Part-time employment is based on national definitions.  The definition of part-time work varies considerably across OECD countries Essentially three main approaches can be distinguished: i) a classification based on the worker’s perception of his/her employment situation; ii) a cut-off (generally 30 or 35 hours per week) based on usual working hours, with persons usually working fewer hours being considered part-timers; iii) a comparable cut-off based on actual hours worked during the reference week. A criterion based on actual hours will generally yield a part-time rate higher than one based on usual hours, particularly if there are temporary reductions in working time as a result of holidays, illness, short-timing, etc. On the other hand, it is not entirely clear whether a classification based on the worker’s perception will necessarily yield estimates of part-time work that are higher or lower than one based on a fixed cut-off. In one country (France) which changed from 1981 to 1982 from a definition based on an actual hours cut-off (30 hours) to one based on the respondent’s perception, the latter criterion appeared to produce slightly higher estimates.

The Global Burden of Disease Study 2015 (GBD 2015), coordinated by the Institute for Health Metrics and Evaluation (IHME), estimated the burden of diseases, injuries, and risk factors at the global, regional, national, territorial, and, for a subset of countries, subnational level. This dataset measures progress towards the Millennium Development Goal 5 (MDG 5) target of a 75% reduction in the maternal mortality ratio between 1990 and 2015. Maternal mortality ratio estimates for 21 regions, 195 countries and territories and 4 United Kingdom subnational units for 1990-2015 (quinquennial) are available by age and cause from the GBD Results Tool. Files available in this record include tables published in The Lancet in October 2016 in "Global, regional, and national levels of maternal mortality, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015.

Data cited at: Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2017 (GBD 2017) Health-related Sustainable Development Goals (SDG) Indicators 1990-2030. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2018.   The Global Burden of Disease Study 2017 (GBD 2017), coordinated by the Institute for Health Metrics and Evaluation (IHME), estimated the burden of diseases, injuries, and risk factors from 1990 to 2017. The United Nations established, in September 2015, the Sustainable Development Goals (SDGs), which specify 17 universal goals, 169 targets, and 232 indicators leading up to 2030. Drawing from GBD 2017, this dataset provides estimates on progress for 41 health-related SDG indicators for 195 countries and territories from 1990 to 2017, and projections, based on past trends, for 2018 to 2030. Estimates are also included for the health-related SDG index, a summary measure of overall performance across the health-related SDGs.

Data citation: Data retrieved from the Area Database of the Global Data Lab, https://globaldatalab.org/areadata/, Version 4.4.1Smits, J. GDL Area Database. Sub-national development indicators for research and policy making.Smits GDL Working Paper 16-101 v44.pdf Smits GDL Working Paper 16-101 v44.docx

General and Life Insurance Sector Statistics of India

General Census of Population and Housing (RGPH) 2013 - http://www.ons.mr/images/Archive/images/rgph2013/Chapitres_RGPH_Fr/

Chemistry facts and figures 2017: Investments

NOTICE (March 2018): This website provides access to the CDIAC archive data temporarily. It will be gradually transitioned into data packages in the new ESS-DIVE archive. This site will continue to operate in parallel during and after the transition, and will be retired at a future date. If you have any questions regarding the data or the transition, please contact [email protected]. The carbon-cycle model simulates vegetation, litter, and soil carbonin 12 ecosystem and land-use classifications aggregated to 14 world regions. Land-use transitions are calculated annually on a 0.5 degree grid before aggregation to the regional level. Secondary forest re-growth is simulated in 50-year age classes for non-boreal forest (labeled as 1500_OtherForest, for forest that started regrowing in the period 1500-1549). Carbon stocks as land-use changes (e.g. conversion to cropland or reversion to native ecosystems) are explicitly tracked over time. Regional cropland areas, productivity, and harvest index change over time as estimated from historical data.

This database contains statistics on production volume and value by species, country or area, fishing area and culture environment

The Global Burden of Disease Study 2015 (GBD 2015), coordinated by the Institute for Health Metrics and Evaluation (IHME), estimated the burden of diseases, injuries, and risk factors at the global, regional, national, territorial, and, for a subset of countries, subnational level. As part of this study, estimates for obesity and overweight prevalence and the disease burden attributable to high body mass index (BMI) were produced by sex, age group, and year for 195 countries and territories. Estimates for high BMI-attributable deaths, DALYs, and other measures (1990-2015) are available from the GBD Results Tool. Files available in this record include obesity and overweight prevalence estimates for 1980-2015. Study results were published in The New England Journal of Medicine in June 2017 in "Health Effects of Overweight and Obesity in 195 Countries over 25 Years."

The Global Carbon Project facilitates access to data to encourage its use and promote a good understanding of the carbon cycle. Respecting original data sources is key to help secure the support of data providers to enhance, maintain and update valuable data. 

This database contains statistics on the annual production of fishery commodities and imports and exports of fishery commodities by country and commodities in terms of volume and value from 1976.

The average global index score stagnated in 2022. Despite expectations of a rebound after the lifting of pandemic-related restrictions, the score was almost unchanged, at 5.29 (on a 0-10 scale), compared with 5.28 in 2021. The positive effect of the restoration of individual freedoms was cancelled out by negative developments globally. The scores of more than half of the countries measured by the index either stagnated or declined. Western Europe was a positive outlier, being the only region whose score returned to pre-pandemic levels. 

The Grant Thornton Global Dynamism Index (GDI) ranks 60 leading economies on their dynamism in five key areas – business operating environment, economics & growth, science & technology, labour & human capital and financing environment. The GDI analyses 22 indicators across these five categories to assess the dynamism of business growth environments around the world, where dynamism refers to the changes in an economy over the past 12 months which are likely to lead to a faster future rate of growth.

Data cited at: Economic Policy Uncertainty The Global Economic Policy Uncertainty (GEPU) Index is a GDP-weighted average of national EPU indices for 20 countries: Australia, Brazil, Canada, Chile, China, France, Germany, Greece, India, Ireland, Italy, Japan, Mexico, the Netherlands, Russia, South Korea, Spain, Sweden, the United Kingdom, and the United States.

This dataset includes world and regional investment data for supply (fossil fuels, renewables, electricity networks, other) and end-use (energy efficiency, renewables and other)

The GEM Adult Population Survey (APS) measures the level and nature of entrepreneurial activity around the world. It is administered to a representative national sample of at least 2000 respondents. The Global Entrepreneurship Monitor is the world's foremost study of entrepreneurship. Through a vast, centrally coordinated, internationally executed data collection effort, GEM is able to provide high quality information, comprehensive reports and interesting stories, to enhance the understanding of the entrepreneurial phenomenon.

Data cited at:  GEM National Expert Survey The GEM National Expert Survey (NES) monitors the factors that are believed to have a significant impact on entrepreneurship, known as the Entrepreneurial Framework Conditions (EFCs). It is administered to a minimum of 36 carefully chosen 'experts' in each country. The Global Entrepreneurship Monitor is the world's foremost study of entrepreneurship. Through a vast, centrally coordinated, internationally executed data collection effort, GEM is able to provide high quality information, comprehensive reports and interesting stories, to enhance the understanding of the entrepreneurial phenomenon.

Research by the Global Burden of Disease Health Financing Collaborator Network produced projected health spending estimates for 2022-2050 for 204 countries and territories. The estimates cover total health spending, health spending disaggregated by source into three domestic financing source categories (government, out-of-pocket, and prepaid private), and development assistance for health (DAH). Retrospective health spending estimates for 1995-2022 and key covariates (including GDP per capita, total government spending, total fertility rate, and fraction of the population older than 65 years) were used to forecast GDP and health spending through 2050. Estimates are reported in constant 2022 US dollars, constant 2022 purchasing-power parity-adjusted (PPP) dollars, and as a percent of gross domestic product. 

Data cited at: The World Bank https://datacatalog.worldbank.org/ Topic: Global Financial Development Publication: https://datacatalog.worldbank.org/dataset/global-financial-development License: http://creativecommons.org/licenses/by/4.0/   The Global Financial Development Database is an extensive dataset of financial system characteristics for 206 economies. The database includes measures of (1) size of financial institutions and markets (financial depth), (2) degree to which individuals can and do use financial services (access), (3) efficiency of financial intermediaries and markets in intermediating resources and facilitating financial transactions (efficiency), and (4) stability of financial institutions and markets (stability).For a complete description of the dataset and a discussion of the underlying literature, see: Martin Cihak; Asli Demirguc-Kunt; Erik Feyen; and Ross Levine, 2012. "Benchmarking Financial Systems Around the World." World Bank Policy Research Working Paper 6175, World Bank, Washington, D.C.

Data cited at: The World Bank https://datacatalog.worldbank.org/ Topic: Global Financial Inclusion (Global Findex) Database Publication: https://datacatalog.worldbank.org/dataset/global-financial-inclusion-global-findex-database License: http://creativecommons.org/licenses/by/4.0/   The Global Financial Inclusion Database provides 850+ country-level indicators of financial inclusion summarized for all adults and disaggregated by key demographic characteristics-gender, age, education, income, employment status and rural residence. Covering more than 140 economies, the indicators of financial inclusion measure how people save, borrow, make payments and manage risk. The reference citation for the data is: Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. World Bank: Washington, DC.

Contains global production statistics (capture and aquaculture). This database contains the volume of aquatic species caught by country or area, by species items, by FAO major fishing areas, and year, for all commercial, industrial, recreational and subsistence purposes. The harvest from maricultural, aquaculture and other kinds of fish farming is also included

Global Footprint Network National Footprint Accounts, 2019 Edition Downloaded on 2 October 2020 from http://data.footprintnetwork.org.

DescriptionThe Global Forest Resources Assessment 2015 (FRA 2015) is the most comprehensive assessment of forests and forestry to date - not only in terms of the number of countries and people involved - but also in terms of scope. It examines the current status and recent trends for about 90 variables covering the extent, condition, uses and values of forests and other wooded land, with the aim of assessing all benefits from forest resources. Information has been collated from 233 countries and territories for four points in time: 1990, 2000, 2005 and 2010. The results are presented according to the seven thematic elements of sustainable forest management. FAO worked closely with countries and specialists in the design and implementation of FRA 2010 - through regular contact, expert consultations, training for national correspondents and ten regional and subregional workshops. More than 900 contributors were involved, including 178 officially nominated national correspondents and their teams. The outcome is better data, a transparent reporting process and enhanced national capacity in developing countries for data analysis and reporting. The final report of FRA 2010 was published at the start of the latest biennial meeting of the FAO' Committee on Forestry and World Forest Week, in Rome.

Direct greenhouse gases: Carbon Dioxide (CO2), Methane (CH4), Nitrous Oxide (N2O), Hydrofluorocarbons (HFC-23, 32, 125, 134a, 143a, 152a, 227ea, 236fa, 245fa, 365mfc, 43-10-mee), Perfluorocarbons (PFCs: CF4, C2F6, C3F8, c-C4F8, C4F10, C5F12, C6F14, C7F16), Sulfur Hexafluoride (SF6), Nitrogen Trifluoride (NF3) and Sulfuryl Fluoride (SO2F2). Emissions are calculated by individual countries using country-specific information. The countries are organized in different world regions for illustration purposes. Emissions of some small countries are presented together with other countries depending on country definition and availability of activity statistics. Source: European Commission, Joint Research Centre (JRC)/PBL Netherlands Environmental Assessment Agency.

This dataset presents a collection of key ICT indicators for about 200 economies worldwide, covering statistics on access to and use of ICTs by households and individuals.

Note: It has been observed that LMC Automotive has been acquired by GlobalData, and no relevant/latest data is available publically. Refer https://www.globaldata.com/industries-we-cover/automotive/  for further information.

FAO - GLEAM Global greenhouse gas emissions from livestock summary data (2017)

In 2020, global LNG imports reached 356.1 million tons (MT), increasing by 1.4 MT or 0.4% compared with the previous year. Despite the challenges imposed by the pandemic, LNG trade has proven resilient, increasingly diverse and global. Trade continued to grow strongly in the first quarter of 2020 but as demand weakened in the second and third quarters, supply-side adjustments helped balance the market.

This data set include information of The global packaging industry turned over around $485 billion in 2004 with packaging container sales of almost $460 billion and machinery sales of around $25 billion. Used in a wide range of industries across food and drink, healthcare, cosmetics and other consumer goods as well as a range of industrial sectors, packaging has become an essential everyday item, with its usage growing broadly in line with the global economy.Constant prices from 2005 onwards; totals may not add up due to rounding

These totals include both battery electric vehicles, or BEVs, and plug-in hybrid electric vehicles, or PHEVs. They do not include conventional hybrids. RoW stands for rest of the world Source: UN Environment, Bloomberg New Energy Finance. Source: Frankfurt School-UNEP Centre/BNEF. 2018. Global Trends in Renewable Energy Investment 2018, http://www.fs-unep-centre.org (Frankfurt am Main)

Global Trends in Prevalence of Tobacco Smoking 2000-2025

The OECD Green Growth database contains selected indicators for monitoring progress towards green growth to support policy making and inform the public at large. The database synthesises data and indicators across a wide range of domains including a range of OECD databases as well as external data sources. The database covers OECD member and accession countries, key partners (including Brazil, China, India, Indonesia and South Africa) and other selected non-OECD countries.

The GGDC 10-Sector Database provides a long-run internationally comparable dataset on sectoral productivity performance in Asia, Europe, Latin America and the US. Variables covered in the data set are annual series of value added, output deflators, and persons employed for 10 broad sectors. It gives sectoral detail to the historical macro data in Maddison (2003) from 1950 onwards. It consists of series for 10 countries in Asia, 9 in Latin-America and 9 in Europe and the US. The data for Asia and Latin-America are based on Marcel P. Timmer and Gaaitzen J. de Vries (2007), 'A Cross-Country Database For Sectoral Employment And Productivity In Asia And Latin America, 1950-2005', GGDC Research memorandum GD-98, Groningen Growth and Development Centre, August 2007. Data for Europe and the US is based on an update of Bart van Ark (1996), Sectoral Growth Accounting and Structural Change in Post-War Europe, in B. van Ark and N.F.R. Crafts, eds., Quantitative Aspects of Post-War European Economic Growth, CEPR/Cambridge University Press, pp. 84-164. All series derived from this database need to be referred to as: "Timmer, Marcel P. and Gaaitzen J. de Vries (2009), "Structural Change and Growth Accelerations in Asia and Latin America: A New Sectoral Data Set" Cliometrica, vol 3 (issue 2) pp. 165-190."

Health Nutrition and Population Statistics database provides key health, nutrition and population statistics gathered from a variety of international and national sources. Themes include global surgery, health financing, HIV/AIDS, immunization, infectious diseases, medical resources and usage, noncommunicable diseases, nutrition, population dynamics, reproductive health, universal health coverage, and water and sanitation.

This dataset presents HNP data by wealth quintile since 1990s to present. It covers more than 70 indicators, including childhood diseases and interventions, nutrition, sexual and reproductive health, mortality, and other determinants of health, for more than 90 low- and middle-income countries. The data sources are Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS).

This data set contains information of The statistics yearbook 2017 is compiled and published by Lao statistics Bureau, Ministry of Planning and Investment. Lao statistics Bureau of expressing gratitude provincial statistics Centre, line ministries involved, ministry-equivalent agencies and other parties to contribute to compile statistical data Economic, Social and Natural Resources and Environment cooperation throughout, Lao Statistics Bureau welcome the proposal, comments and feedback to guide the improvement this magazines better. Meanwhile, the Lao Statistics Bureau express thanks to highly among those interested in using information and statistics to Lao Statistics Bureau. TRANSLATE with xEnglishArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian  TRANSLATE with COPY THE URL BELOW BackEMBED THE SNIPPET BELOW IN YOUR SITEEnable collaborative features and customize widget: Bing Webmaster PortalBack

Global Burden of Disease Study 2016 (GBD 2016) Healthcare Access and Quality Index Based on Amenable Mortality 1990–2016. Global Burden of Disease Study 2016 (GBD 2016) estimates were used in an analysis of personal healthcare access and quality for 195 countries and territories, as well as selected subnational locations, over time. This dataset includes the following global, regional, national, and selected subnational estimates for 1990-2016: age-standardized risk-standardized death rates from 24 non-cancer causes considered amenable to healthcare; age-standardized mortality-to-incidence ratios for 8 cancers considered amenable to healthcare; and the Healthcare Access and Quality (HAQ) Index and individual scores for each of the 32 causes on a scale of 0 to 100. Code used to produce the estimates is also included. Results were published in The Lancet in May 2018 in "Measuring performance on the Healthcare Access and Quality Index for 195 countries and territories and selected subnational locations: a systematic analysis from the Global Burden of Disease Study 2016

This dataset presents annual population data from 1960 by sex and five year age groups as well as the share of children, youth, the elderly, old-age and total dependency ratios. The data is available for all member countries as well as for the EU27 and G20 countries, Singapore and the World total. The data refer to mid-year estimates of the population.

This table presents an overview of the number of guests and their overnight stays in the Netherlands in all hotels, motels, boarding houses, apartments with hotel services, youth accommodation and bed & breakfasts with at least 5 sleeping places. The figures can be broken down by country of residence of the guest, and by star rating. Figures are available for The Netherlands as a whole, and for the city of Amsterdam. The breakdown by star rating is based on the opinion of the accommodation itself. The star rating does not have to be officially registered. The breakdown contains all types of accommodation mentioned above, not just hotels. The '5 stars' category contains 5 star hotels, but also for instance 5 star bed&breakfasts. Break in series: Figures on guests and overnight stays per star rating for the years until 2015, that were published before, were based on offical registrations of the number of stars by the 'Bedrijfschap Horeca en Catering'. This official registration does no longer exist. Therefore, Statistics Netherlands started asking accommodations about their number of stars in its annual survey. For this reason, the figures in this table are not directly comparable with figures published about the years until 2015. Data available from: 2017 Status of the figures: All figures are provisional. Changes as of 9 January 2019: Figures for October 2018 have been added. When will new figures be published? Figures of a new month become available within three months after the end of that month, these are provisional figures. The figures for the complete year are revised one month after publication of the December figures, these are revised provisional figures. Two months later definite figures will be published.

Data cited at:  CBS StatLine databank https://opendata.cbs.nl/statline/portal.html?_la=en&_catalog=CBS Publication: Hotels; guests, overnight stays, country of residence, region https://opendata.cbs.nl/portal.html?_la=en&_catalog=CBS&tableId=82061eng&_theme=1026 License: http://creativecommons.org/licenses/by/4.0/   This table presents an overview of the number of guests and their overnight stays in the Netherlands in all hotels, motels, boarding houses, apartments with hotel services, youth accommodation and bed & breakfasts with at least 5 sleeping places. The figures can be broken down by country of origin of the guests (country of residence), parts of the country, provinces, tourist parts of the country and 5 major cities. Data available from: 2012 Status of the figures: The figures for the most recent reporting year 2018 are provisional. The figures until 2017 are final. Changes as of 9 January 2019: Figures for October 2018 have been added. The table now contains separate figures for the municipality of Haarlemmermeer. When will new figures be published? Figures of a new month become available within three months after the end of that month, these are provisional figures. The figures for the complete year are revised one month after publication of the December figures, these are revised provisional figures. Two months later definite figures will be published.

This table provides a detailed breakdown of the financial assets and liabilities of households and non-profit institutions serving households (NPISH) by financial instrument. On the asset side of the balance sheet, it shows data on households’ and NPISHs’ holdings of investment fund shares, life insurance and annuity entitlements, and pension entitlements; and on the liability side, it shows data on their short- and long-term borrowing (loans).

Data source(s) used: Survey on income and living conditions in immigrant families The English description of the source is not available at this time, for methodological details go to the Siqual system

Data cited at: International Telecommunication Union-https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx 

The sources for this database are mainly census data, from the 2000 round of censuses. Census data were used for 22 countries. Countries not taking periodic censuses but keeping population registers have provided data extracted from these registers; this is the case for four countries: Denmark, Finland, Norway and Sweden. For some countries, not all themes covered in the database are present in the national census or register. Labour force surveys, provided by Eurostat and averaged over the period 1998-2002, have been used to fill the gaps where possible.

The sources for this database are mainly census data, from the 2000 round of censuses. Census data were used for 22 countries. Countries not taking periodic censuses but keeping population registers have provided data extracted from these registers; this is the case for four countries: Denmark, Finland, Norway and Sweden. For some countries, not all themes covered in the database are present in the national census or register. Labour force surveys, provided by Eurostat and averaged over the period 1998-2002, have been used to fill the gaps where possible.

The sources for this database are mainly census data, from the 2000 round of censuses. Census data were used for 22 countries. Countries not taking periodic censuses but keeping population registers have provided data extracted from these registers; this is the case for four countries: Denmark, Finland, Norway and Sweden. For some countries, not all themes covered in the database are present in the national census or register. Labour force surveys, provided by Eurostat and averaged over the period 1998-2002, have been used to fill the gaps where possible. The exact national source and reference period for each file is given in Table A.1 (see the methodological document).

Statistics Name: Current Survey of Supply and Demand for Petroleum Products Annual Report Petroleum Products Import and Export of Petroleum Products/Monthly Import by Area and Country F.Y.

Monthly data on Oil Products Imports (by country) in Japan.  

The allocation of bilateral intermediate imports across using industries assumes that import coefficients are the same for all trade partners, i.e. SHAREipkt is identical across exporter countries. Hence, the bilateral pattern of imported intermediates from industry p is the same across all using industries k. However, it is different from the bilateral pattern of total imports from industry p because trade data (measured by VALUEijpt) allows distinguishing bilateral imports of intermediates from final good imports in industry p. While the BEC classification enables the identification of intermediate goods, no similar classification is available for trade in services, due to the high level of aggregation in services trade data. While goods trade data are based on customs declarations allowing the identification of goods at a highly disaggregated level, services trade data are based on a variety of information such as business accounts, administrative sources, surveys, and estimation techniques (Manual on Statistics of International Trade in Services, 2002). Hence, in the case of trade in services, VALUEijpt is the total value of imports of service p, i.e. both final and intermediate (and not only services that are used in the production of other goods and services, as in the case of goods data). By making an additional assumption and adjusting SHAREipkt, it is however possible to calculate trade in intermediate services. In the case of services imports, SHAREipkt is the share of imported service inputs p used by industry k in total imports of p of country i. In the case of services, besides the assumption that all trading partners have the same distribution of intermediate imports p across using industries k, it is furthermore required that the share of intermediate services in overall bilateral services imports of country i is the same across all partner countries j. Finally, it should be mentioned that trade data reported in the trade statistics do not fully match imports as reported in I-O tables. One main reason is that while trade data is recorded at consumer prices, I-O tables are evaluated at producer prices. There are also other differences such as the treatment of re-exports, scrap metal, waste products and second hand goods or unallocated trade data.

Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. The inactivity rate is the number of persons of working age outside the labour force (that is, not employed or unemployed) expressed as a percentage of the working-age population. The series is part of the ILO modelled estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILOSTAT pages on concepts and definitions and ILO modelled estimates and projections.

Inbound tourism comprises the activities of a non-resident visitor within the country of reference.

This table contains the shares of economic short-time workers among total employment, the ratio of economic short-time workers and labour force, and the gender composition of economic short-time workers. Data re broken down by professional status - employees, total employment – by sex and standardised age groups (15-24, 25-54, 55+, total). Unit of measure used - Data are expressed as percentages.

Incidence of employment by usual weekly hours worked: This table contains data on the cross-country distribution of employment by hour bands for declared hour bands, broken down by professional status - employees, total employment - sex and detailed age groups. In order to facilitate analysis and comparisons over time, historical data for OECD members have been provided over as long a period as possible, often even before a country became a member of the Organisation. Information on the membership dates of all OECD countries can be found at OECD Ratification Dates

This table contains incidences and gender composition of part-time employment with standardised (15-24, 25-54, 55-64, 65+, total) and detailed age groups. Data are further broken down by professional status - employees, total employment. Part-time employment is based on a common 30-usual-hour cut-off in the main job. Unit of measure used - Data are expressed in percentages.

This table contains incidences and gender composition of part-time employment with standardised (15-24, 25-54, 55-64, 65+, total) and detailed age groups. Data are further broken down by professional status - employees, total employment. Part-time employment is based on national definitions. The definition of part-time work varies considerably across OECD countries Essentially three main approaches can be distinguished: i) a classification based on the worker's perception of his/her employment situation; ii) a cut-off (generally 30 or 35 hours per week) based on usual working hours, with persons usually working fewer hours being considered part-timers; iii) a comparable cut-off based on actual hours worked during the reference week. A criterion based on actual hours will generally yield a part-time rate higher than one based on usual hours, particularly if there are temporary reductions in working time as a result of holidays, illness, short-timing, etc. On the other hand, it is not entirely clear whether a classification based on the worker's perception will necessarily yield estimates of part-time work that are higher or lower than one based on a fixed cut-off. In one country (France) which changed from 1981 to 1982 from a definition based on an actual hours cut-off (30 hours) to one based on the respondent's perception, the latter criterion appeared to produce slightly higher estimates. Other data characteristics

This table contains incidences and gender composition of temporary employment with standardized age groups (15-24, 25-54, 55-64, 65+, total). Data are further broken down by professional status - employees, total employment. Unit of measure used - Data are expressed in percentages.